Introduction

This script reproduces the work on modeling blood trajectories in spinal cord injury (SCI) by the Health.data DRIVEN lab at the School of Public Health Sciences at the University of Waterloo.

Three datasets are used in this work: MIMIC-III version 1.4, MIMIC-IV version 1.0, and a subset of the TRACK-SCI cohort study. Please see The dataset build section below to have information on obtaining and organizing those datasets.

Note 1. We do not provide the datasets directly, and users of this code will need to download the data. The script contains the necessary code to prepare the data for analysis.

Note 2. Some sections of the scripts are not evaluated during knitting (rendering) due to their computational overhead. We have provided intermediate files containing the necessary objects and biproducts of the code, including the final trajectory models to facilitate reproducibility. To reproduce this work, you will need to run the code on the IDE by chunck.

Note 3. Running the full script from scratch will override some of the provided files and it can take hours to days to complete.

Setup

This creates folder structure for needed sub-folders if they do not exist

sub_dir<-c("figures", "data", "models", "tables", 
           "figures/GMM fit plots", "figures/Variable Importance plots",
           "tables/GMM fit tables",
           "prediction_experiments")
lapply(sub_dir, function(x){
  if (!dir.exists(x)) {dir.create(x)}
})

Libraries

Dependencies

This work should be reproducible under the following system configuration

"R version 4.4.1 (2024-06-14 ucrt)"

"tidyverse 2.0.0"
"data.table 1.17.0"
"stringr 1.5.1"
"DT 0.33"
"gtsummary 2.2.0"
"lcmm 1.9.4"
"caret 7.0-1"
"yardstick 1.3.2"
"patchwork 1.3.0"
"parallel (base with R 4.4.1)"

Use this line of code to install the 1.9.4 version of the lcmm package needed to reproduce this work.

# install.packages("http://cran.us.r-project.org/src/contrib/Archive/lcmm/lcmm_1.9.4.tar.gz",
#                  repos=NULL, type="source")

Load libraries

# Core data manipulation and visualization libraries
library(tidyverse)
library(ggpubr)

library(data.table)    
library(stringr)       
# library(lubridate)     

# Table and reporting tools
library(DT)            
library(kableExtra)    
library(gtsummary)     

# Modeling libraries
library(lcmm)          # Latent class mixed models (trajectory modeling), note that version 1.9.4 is needed. Latest version have change considerable and will not work with this code.
library(caret)         # Traditional ML framework with training/tuning
library(yardstick)    # for performance

# Visualization enhancements
library(patchwork)     

# Parallel processing
library(parallel)


source("functions_ed2.R") #this file contains auxiliary custom functions

The dataset build

Create a data folder in the root folder for this project, and add a sub-folder for MIMIC-III, MIMIC-IV, and TRACK-SCI. The script reproducing the work has relative path to those folders.

Datasets

MIMIC

Data has been download from PhysioNet. Both MIMIC databases (DB) are relational DB structured in tables. Documentation about the DB schema can be found here.

Note that data access need Data Use Agreement with PhysioNet. No data is provided in this document or repository. The code would not run without the data!

For cohort selection, we used the International Statistical Classification of Disease (ICD) codes, version 9/10. From the respective DB, the following tables should be downloaded and locally accessible:

MIMIC-III

PATIENTS: information demographics for each patient in the DB
ADMISSIONS: Unique hospitalizations of each patient in the DB
DIAGNOSES_ICD: Hospital assigned diagnoses with ICD codes
LABEVENTS: Laboratory measurements for patients
D_LABITEMS: dictionary with each laboratory assay metadata
D_ICD_DIAGNOSES: definitions for ICDs diagnoses

MIMIC-IV

core/patients: information demographics for each patient in the DB
core/admissions: Unique hospitalizations of each patient in the DB
hosp/diagnoses_icd: Hospital assigned diagnoses with ICD codes
hosp/labevents: Laboratory measurements for patients
hosp/d_labitems: dictionary with each laboratory assay metadata
hosp/d_icd_diagnoses: definitions for ICDs diagnoses

TRACK-SCI

The necessary TRACK-SCI data can be downloaded from the Open Data Commons for Spinal Cord Injury (SCI) here. If you use the data, please cite:

Mussavi Rizi, M., Saigal, R., DiGiorgio, A. M., Ferguson, A. R., Beattie, M. S., Kyritsis, N., Torres Espin, A.. 2025. Blood laboratory values from 137 de-identified TRACK-SCI participants from routine collected real-world data. Open Data Commons for Spinal Cord Injury. ODC-SCI:1345. doi: 10.34945/F5PK6X

MIMIC Patient selection

Note. The list of ICD9 can be seen in the icd9_codes_sci.csv, and ICD10 can be seen in the icd10_codes_sci.csv

In MIMIC diagnostics table, there is one row per diagnostic per patient per hospital admission. Some patients may have more than one hospital admission. We selected the first hospital admission per patient that shows in the DB. From there, we filtered the patients that have presence of selected ICD9/10 codes in their hospital stay.

Creating ICD9/10 filtering

MIMIC-III/IV uses both ICD9 and 10. For filtering, we created a separated list of ICD9 and ICD10 codes in two .csv files. Here we upload the files and create a character vector with all the codes for the filtering. We append \\b in front of each code as this helps with the processes of filtering by regular expressions. \\b ensures that each code matches from the beginning of the code, preventing middle code mismatches since some times codes are truncated or has extensions.

# Load ICD-9 and ICD-10 SCI-related diagnosis codes
icd9_codes_sci <- read.csv("icd9_codes_sci.csv")
icd10_codes_sci <- read.csv("icd10_codes_sci.csv")

# Adds word boundary markers (\\b) to ensure exact code matches
icd_scAll_filter <- paste(
  c(
    paste0("\\b", unique(icd9_codes_sci$icd9_codes_L1)),
    paste0("\\b", unique(icd10_codes_sci$icd10_codes_L1))
  ),
  collapse = "|"
)

# Preview the generated regex filter
head(icd_scAll_filter)

## [1] "\\b952|\\b805|\\b953|\\b806|\\bS120|\\bS121|\\bS122|\\bS123|\\bS124|\\bS125|\\bS126|\\bS128|\\bS129|\\bS140|\\bS141|\\bS142|\\bS220|\\bS240|\\bS241|\\bS242|\\bS320|\\bS321|\\bS340|\\bS341|\\bS342|\\bS343"

MIMIC III

Not computed during knitting

# Load core MIMIC-III tables
mimic3_diagnostic <- read.csv("data/MIMICIII/DIAGNOSES_ICD.csv.gz")
mimic3_admissions <- read.csv("data/MIMICIII/ADMISSIONS.csv.gz")
mimic3_patients   <- read.csv("data/MIMICIII/PATIENTS.csv.gz")

# Convert SUBJECT_ID to character and drop ROW_ID for consistency
mimic3_patients <- mimic3_patients %>%
  mutate(SUBJECT_ID = as.character(SUBJECT_ID)) %>%
  select(-ROW_ID)

# Identify hospital admissions with SCI-related ICD-9 codes
mimic3_diagnostic_sci9 <- mimic3_diagnostic %>%
  filter(str_detect(ICD9_CODE, icd_scAll_filter))

# Extract unique hospital admissions (HADM_IDs) for SCI patients
mimic3_sci_HADM_ID <- unique(mimic3_diagnostic_sci9$HADM_ID)

# Aggregate diagnosis info for SCI-related admissions
mimic3_diagnostic_sci9 <- mimic3_diagnostic %>%
  filter(HADM_ID %in% as.numeric(mimic3_sci_HADM_ID)) %>%  # Filter by SCI-relevant admissions
  mutate(SUBJECT_ID = as.character(SUBJECT_ID)) %>%
  arrange(SEQ_NUM, SUBJECT_ID) %>%
  group_by(HADM_ID) %>%
  mutate(n_codes = n()) %>%  # Number of unique diagnosis codes per admission
  summarise_all(.funs = function(x) paste(unique(x), collapse = " | ")) %>%
  select(-ROW_ID)

# Count how many patients have more than one hospital admission
sum(duplicated(mimic3_diagnostic_sci9$SUBJECT_ID))

We see that 13 patients had more than one hospital admission. We select the first admission using the information of the admission table.

# Filter MIMIC-III admissions to retain only SCI-relevant hospitalizations
mimic3_filtered <- mimic3_admissions %>%
  mutate(SUBJECT_ID = as.character(SUBJECT_ID)) %>%
  right_join(mimic3_diagnostic_sci9) %>% # Retain only SCI-related admissions
  left_join(mimic3_patients) %>%
  arrange(EDREGTIME) %>%  
  group_by(SUBJECT_ID) %>%
  mutate(n_hadm = n()) %>%  # Count number of admissions per patient
  filter(n_hadm == 1, !is.na(EDREGTIME), EDREGTIME != "") %>%  # Keep only patients with a single admission and valid time
  select(-ROW_ID)

Calculating age in MIMIC-III

MIMIC-III does not provide age. Dates for each patient are shifted for privacy reasons, but temporal consistency is maintained. We can calculate age at hospital arrival by subtracting date of birth from arrival time (ED register time).

# Convert date-time columns and compute patient age at admission
mimic3_filtered <- mimic3_filtered %>%
  mutate(
    DOB = strptime(DOB, format = "%Y-%m-%d %H:%M:%S"),
    EDREGTIME = strptime(EDREGTIME, format = "%Y-%m-%d %H:%M:%S"),
    age = as.numeric(difftime(EDREGTIME, DOB, units = "days")),  
    age = floor(age / 365),  
    age = ifelse(age >= 300, 89, age)  # Cap extreme/unknown ages at 89
  )

# Summarize age distribution
summary(mimic3_filtered$age)

Save data object

# Save the filtered dataset for downstream analysis
saveRDS(mimic3_filtered, file = "data/MIMICIII/mimic3_filtered.Rds")

# Load pre-filtered MIMIC-III dataset
mimic3_filtered <- readRDS("data/MIMICIII/mimic3_filtered.Rds")

We can see that there are no kids (minimal age is 15) in the dataset. We can also see that the max is 303. This is due to the fact that, for privacy reasons, ages above 89 are shifted to 300. No further filter is required.

The number of unique patients in the MIMIC-III dataset is 1194

MIMIC IV

Not computed during knitting

## Load core MIMIC-IV tables
mimic4_diagnostic <- read.csv("data/MIMICIV/diagnoses_icd.csv.gz")
mimic4_admissions <- read.csv("data/MIMICIV/admissions.csv.gz")
mimic4_patients   <- read.csv("data/MIMICIV/patients.csv.gz")

# Ensure subject IDs are treated as character strings for consistency across tables
mimic4_admissions <- mimic4_admissions %>%
  mutate(subject_id = as.character(subject_id))

mimic4_patients <- mimic4_patients %>%
  mutate(subject_id = as.character(subject_id))

## Identify hospital admissions with SCI-related ICD codes (ICD-9 or ICD-10)
mimic4_diagnostic_sc <- mimic4_diagnostic %>%
  filter(str_detect(icd_code, icd_scAll_filter))

# Extract unique hospital admissions (hadm_id) for SCI patients
mimic4_sci_hadm_id <- unique(mimic4_diagnostic_sc$hadm_id)

## Aggregate diagnosis info for SCI-related admissions
mimic4_diagnostic_sc <- mimic4_diagnostic %>%
  filter(hadm_id %in% as.numeric(mimic4_sci_hadm_id)) %>%
  mutate(subject_id = as.character(subject_id)) %>%
  arrange(seq_num, subject_id) %>%
  group_by(hadm_id) %>%
  mutate(n_codes = n()) %>%
  summarise_all(.funs = function(x) paste(unique(x), collapse = " | "))

# Count how many patients have more than one hospital admission
sum(duplicated(mimic4_diagnostic_sc$subject_id))

488 patients had more than one hospital admission. We will select the first admission using the information of the admission table.

# Join and filter MIMIC-IV data to retain only first SCI-related admission per patient
mimic4_filtered <- mimic4_admissions %>%
  mutate(subject_id = as.character(subject_id)) %>%
  right_join(mimic4_diagnostic_sc) %>%  # Retain only SCI-related admissions
  left_join(mimic4_patients) %>%
  arrange(edregtime) %>%   
  group_by(subject_id) %>%
  mutate(n_hadm = 1:n()) %>%  
  filter(n_hadm == 1, edregtime != "")  # Keep only first valid admission

Patient Age

In MIMIC-IV, age at arrival to the hospital is calculated by adding the patient’s anchor_age (their age in the anchor_year) to the difference between the year of admission and the anchor_year.

# Convert date-time columns and compute patient age at admission
mimic4_filtered <- mimic4_filtered %>%
 mutate(
  anchor_datetime = make_datetime(year = anchor_year, month = 1, day = 1),
  age = as.numeric(time_length(interval(anchor_datetime, edregtime), "years") + anchor_age),
  age = floor(age),
  age = if_else(age >=89, 89, age)  # Cap extreme/unknown ages at 89
 )

# Summarize age distribution
summary(mimic4_filtered$age)

Save data object

saveRDS(mimic4_filtered, file = "data/MIMICIV/mimic4_filtered.Rds")

#read data
mimic4_filtered <- readRDS("data/MIMICIV/mimic4_filtered.Rds")

The number of unique patients in the MIMIC-IV dataset is 4529

Join both MIMIC

Here we join both datasets with the filtered patient information. One problem at joining both datasets is that during the transition between MIMIC-III and MIMIC-IV, patient identification changed, and both datasets may overlap on a subset of patients. MIMIC-III has patients from 2001 to 2012, MIMIC-IV has patients from 2008 to 2019. In order to prevent patient duplication, we decided to filter out MIMIC-IV patients that contains the same ICD sequence (order, codes and length).

# Count how many MIMIC-IV diagnosis codes also appear in the filtered MIMIC-III ICD-9 codes
sum(mimic4_filtered$icd_code %in% mimic3_filtered$ICD9_CODE)

## [1] 515

# Exclude overlapping ICD codes from MIMIC-IV if they are present in MIMIC-III
mimic4_filtered <- mimic4_filtered[!mimic4_filtered$icd_code %in% mimic3_filtered$ICD9_CODE, ]

This reduces the MIMIC-IV in 515 patients, all from the overlapping years

Joining

colnames(mimic3_filtered)

##  [1] "SUBJECT_ID"           "HADM_ID"              "ADMITTIME"           
##  [4] "DISCHTIME"            "DEATHTIME"            "ADMISSION_TYPE"      
##  [7] "ADMISSION_LOCATION"   "DISCHARGE_LOCATION"   "INSURANCE"           
## [10] "LANGUAGE"             "RELIGION"             "MARITAL_STATUS"      
## [13] "ETHNICITY"            "EDREGTIME"            "EDOUTTIME"           
## [16] "DIAGNOSIS"            "HOSPITAL_EXPIRE_FLAG" "HAS_CHARTEVENTS_DATA"
## [19] "SEQ_NUM"              "ICD9_CODE"            "n_codes"             
## [22] "GENDER"               "DOB"                  "DOD"                 
## [25] "DOD_HOSP"             "DOD_SSN"              "EXPIRE_FLAG"         
## [28] "n_hadm"               "age"

colnames(mimic4_filtered)

##  [1] "subject_id"           "hadm_id"              "admittime"           
##  [4] "dischtime"            "deathtime"            "admission_type"      
##  [7] "admission_location"   "discharge_location"   "insurance"           
## [10] "language"             "marital_status"       "ethnicity"           
## [13] "edregtime"            "edouttime"            "hospital_expire_flag"
## [16] "seq_num"              "icd_code"             "icd_version"         
## [19] "n_codes"              "gender"               "anchor_age"          
## [22] "anchor_year"          "anchor_year_group"    "dod"                 
## [25] "n_hadm"               "anchor_datetime"      "age"

Notice that the variable names are not identical between both, so we had to do some cleaning and harmonization:

Transform MIMIC-III colnames to lower case
Change MIMIC-III ICD9_CODE to icd_code
Change MIMIC-IV
Conserve the matching columns
Add a dataset variable identifying the origin
Row bind both datasets

colnames(mimic3_filtered) <- str_to_lower(colnames(mimic3_filtered))

# Ensure ICD code column name matches that of MIMIC-IV
colnames(mimic3_filtered)[20] <- "icd_code"

# Parse datetime columns in MIMIC-III
mimic3_filtered$admittime <- strptime(mimic3_filtered$admittime, 
                                      format = "%Y-%m-%d %H:%M:%S")
mimic3_filtered$edregtime <- strptime(format(mimic3_filtered$edregtime), 
                                      format = "%Y-%m-%d %H:%M:%S")

# Parse datetime columns in MIMIC-IV
mimic4_filtered$admittime <- strptime(mimic4_filtered$admittime, 
                                      format = "%Y-%m-%d %H:%M:%S")
mimic4_filtered$edregtime <- strptime(format(mimic4_filtered$edregtime), 
                                      format = "%Y-%m-%d %H:%M:%S")

# Tag dataset origin
mimic3_filtered$dataset <- "MIMIC-III"
mimic4_filtered$dataset <- "MIMIC-IV"

# Determine common variables to retain
vars_to_keep <- intersect(colnames(mimic3_filtered), colnames(mimic4_filtered))

# Merge datasets based on shared columns
mimic_patients_all <- rbind(
  mimic3_filtered[, vars_to_keep],
  mimic4_filtered[, vars_to_keep]
)

# Count summary
table(mimic_patients_all$dataset)

## 
## MIMIC-III  MIMIC-IV 
##      1194      4014

# Number of unique patients in combined dataset
length(unique(mimic_patients_all$subject_id))

## [1] 5208

MIMIC laboratory marker data

Laboratory data table in both MIMIC-III and IV are too big to work with them on memory. For that we read them in chunks to filter the patients selected above. In addition, the files have been downloaded compressed. To reduce computational burden while reading the file in chunks, we decompressed the files first.

MIMIC-III

Not run during knitting

# Unzip the file. Do only once for the first time to extract the compressed gz file
# R.utils::gunzip("data/MIMICIII/LABEVENTS.csv.gz")


# Define path to MIMIC-III lab events data
mimic3_lab_path <- "data/MIMICIII/LABEVENTS.csv"

# Preview the header to inspect column structure
mimic3_lab_header <- read.csv(mimic3_lab_path, nrows = 1, header = FALSE)
mimic3_lab_header  

# Load and filter lab events data in chunks
mimic3_lab_sc <- read_csv_chunked(
  file = mimic3_lab_path,
  callback = DataFrameCallback$new(mimic3_filter),
  chunk_size = 50000
)

add lab item info

Not run during knitting

# Load lab item metadata (MIMIC-III)
mimic3_lab_items <- read.csv("data/MIMICIII/D_LABITEMS.csv.gz")

# Join lab events with lab item descriptions and clean up
mimic3_lab_sc <- mimic3_lab_sc %>%
  mutate(SUBJECT_ID = as.character(SUBJECT_ID)) %>%
  select(-ROW_ID) %>%
  left_join(mimic3_lab_items, by = "ITEMID") %>%  # Join on lab item ID
  select(-ROW_ID)

Save the data

saveRDS(mimic3_lab_sc, file = "data/MIMICIII/mimic3_lab_sc.Rds")

MIMIC-IV

Not run during knitting

# Unzip the file. Do only once for the first time to extract the compressed gz file
# R.utils::gunzip("data/MIMICIV/labevents.csv.gz")

# Define path to MIMIC-IV lab events data
mimic4_lab_path <- "data/MIMICIV/labevents.csv"

# Preview column headers to verify structure
mimic4_lab_header <- read.csv(mimic4_lab_path, nrows = 1, header = FALSE)
mimic4_lab_header  # Print the header row

# Load and filter lab events in chunks 
mimic4_lab_sc <- read_csv_chunked(
  file = mimic4_lab_path,
  callback = DataFrameCallback$new(mimic4_filter),
  chunk_size = 50000
)

add lab item info

Not run during knitting

# Lab items
mimic4_lab_items <- read.csv("data/MIMICIV/d_labitems.csv.gz")

mimic4_lab_sc <- mimic4_lab_sc %>%
  left_join(mimic4_lab_items)

mimic4_lab_sc <- mimic4_lab_sc %>%
  mutate(subject_id = as.character(subject_id))

Save the data

saveRDS(mimic4_lab_sc, file = "data/MIMICIV/mimic4_lab_sc.Rds")

Join both MIMIC lab data

# Load pre-filtered MIMIC-III lab events and associated lab item data
mimic3_lab_sc <- readRDS("data/MIMICIII/mimic3_lab_sc.Rds")
mimic3_lab_items <- read.csv("data/MIMICIII/D_LABITEMS.csv.gz")

colnames(mimic3_lab_items) <- str_to_lower(colnames(mimic3_lab_items))
mimic3_lab_items$row_id <- NULL  

mimic4_lab_sc <- readRDS("data/MIMICIV/mimic4_lab_sc.Rds")
mimic4_lab_items <- read.csv("data/MIMICIV/d_labitems.csv.gz")

colnames(mimic3_lab_sc) <- str_to_lower(colnames(mimic3_lab_sc))

# Tag dataset origin
mimic3_lab_sc$dataset <- "MIMIC-III"
mimic4_lab_sc$dataset <- "MIMIC-IV"

# Shared variables
var_labs_to_keep <- intersect(colnames(mimic3_lab_sc), colnames(mimic4_lab_sc))

# Combine datasets on shared variables
mimic_labs_all <- rbind(
  mimic3_lab_sc[, var_labs_to_keep],
  mimic4_lab_sc[, var_labs_to_keep]
)

mimic_labs_all$subject_id<-as.character(mimic_labs_all$subject_id)

The time for each item in MIMIC are shifted. We calculated the time of values respect to hospital arrival. For that, we need to join the labs data with the patient data.

# Add admission time info to lab events and compute time since ED registration
mimic_labs_all <- mimic_patients_all %>%
  select(subject_id, hadm_id, edregtime) %>%
  right_join(mimic_labs_all, by = c("subject_id", "hadm_id")) %>%
  ungroup()%>%
  mutate(
    charttime = strptime(format(charttime), format = "%Y-%m-%d %H:%M:%S"),  
    time_minutes = as.numeric(difftime(charttime, edregtime, units = "mins"))  # Time since ED registration
  )

MIMIC patient characteristics

The following section of the code harmonize the selected patient characteristics between both MIMIC datasets.

There are differences in the coding of some variables across datasets. We recoded insurance and ethnicity to match. For ethnicity, we collapsed to the modeling set of levels from MIMIC-IV, for insurance, it is not clear whether Other in IV includes Private and self Pay. We collapsed/recoded as following: Government -> other government, Private, self Pay -> Other

# Harmonize ethnicity and insurance fields in combined MIMIC dataset
mimic_patients_all <- mimic_patients_all%>%
  mutate(
    ethnicity = case_when(
      str_detect(ethnicity, "ASIAN")~"ASIAN",
      str_detect(ethnicity, "BLACK")~"BLACK/AFRICAN AMERICAN",
      str_detect(ethnicity, "HISPANIC")~"HISPANIC/LATINO",
      str_detect(ethnicity, "DECLINED")~NA_character_,
      str_detect(ethnicity, "UNKNOWN|Unknown")~NA_character_,
      is.na(ethnicity)~NA_character_,
      str_detect(ethnicity, "WHITE")~"WHITE",
      str_detect(ethnicity, "OTHER")~"OTHER",
      str_detect(ethnicity, "MULTI")~"MULTI RACE/ETHNICITY"),
    insurance = case_when(
      str_detect(insurance, "Gov")~"Other Government", 
      str_detect(insurance, "Private")~"Other", 
      str_detect(insurance, "Self Pay")~"Other",
      TRUE~insurance)
  )

For encounter characteristics, we will include: admission type, admission location, discharge location. There were clear differences in the names of the levels between datasets. We harmonized these to the minimal information possible.

# Harmonize admission type, admission location, and discharge location fields in MIMIC data
mimic_patients_all <- mimic_patients_all%>%
  mutate(
    admission_type = case_when(
      str_detect(admission_type, "EMER")~"EMERGENCY",
      str_detect(admission_type, "EU")~"EMERGENCY",
      str_detect(admission_type, "OBSER")~"OBSERVATION",
      str_detect(admission_type, "ELECTIVE")~"ELECTIVE",
      str_detect(admission_type, "SURGICAL")~"ELECTIVE",
      TRUE~admission_type),
    admission_location = case_when(
      str_detect(admission_location, "CLINIC")~"CLINIC REFERRAL", 
      str_detect(admission_location, "PHYS")~"CLINIC REFERRAL", 
      str_detect(admission_location, "EMER")~"EMERGENCY ROOM",
      str_detect(admission_location, "INFO")~NA_character_,
      str_detect(admission_location, "TRANS.+HOSP")~"TRANSFER FROM HOSP",
      str_detect(admission_location, "SKILLED")~"TRANSFER FROM SNF",
      TRUE~admission_location),
    discharge_location = case_when(
      discharge_location==""~NA_character_,
      str_detect(discharge_location, "DIED")~"DIED",
      str_detect(discharge_location, "DEAD")~"DIED",
      str_detect(discharge_location, "DISC-TRAN")~"TRANSFER TO OTHER",
      str_detect(discharge_location, "OTHER")~"TRANSFER TO OTHER",
      str_detect(discharge_location, "PSYCH")~"TRANSFER TO OTHER",
      str_detect(discharge_location, "^HOME")~"HOME",
      str_detect(discharge_location, "HOSPICE")~"HOSPICE",
      str_detect(discharge_location, "REHAB")~"REHAB",
      str_detect(discharge_location, "SKILLED")~"SKILLED NURSING FACILITY",
      str_detect(discharge_location, "SNF")~"SKILLED NURSING FACILITY",
      str_detect(discharge_location, "ASSISTED LIVING")~"SKILLED NURSING FACILITY",
      str_detect(discharge_location, "ADVI")~"AGAINST ADVICE",
      str_detect(discharge_location, "LONG TERM")~"LONG TERM CARE",
      str_detect(discharge_location, "SHORT TERM")~"SHORT TERM CARE",
      TRUE~discharge_location)
  )

Filter of patients from Emergency only

With the variables harmonized, we filtered patients admitted to the hospital by emergency.

# Restrict dataset to emergency admissions only
mimic_patients_all <- mimic_patients_all %>%
  filter(admission_type == "EMERGENCY")

# Display number of patients per dataset after filtering
table(mimic_patients_all$dataset)

## 
## MIMIC-III  MIMIC-IV 
##      1193      2955

Cohort definition

We defined three different cohorts depending on their ICD codes. We distinguish between:

Spine Trauma. Spine trauma with no mention of SCI
SCI_Fracture. SCI with spine trauma
SCI_noFracture. SCI with no mention of spine trauma

# Determine patient group

icd_fracture <- "\\b805|\\bS120|\\bS121|\\bS122|\\bS123|\\bS124|\\bS125|\\bS126|\\bS128|\\bS129|\\bS220|\\bS320|\\bS321"

icd_sci <- "\\bS141|\\bS140|\\bS142|\\bS240|\\bS241|\\bS242|\\bS340|\\bS341|\\bS342|\\bS343"

mimic_patients_all <- mimic_patients_all%>%
  group_by(subject_id)%>%
  mutate(cohort_group=case_when(
    str_detect(icd_code, "\\b952|\\b953")&
      !str_detect(icd_code, paste0(icd_fracture,"|\\b806"))~"SCI_noFracture",
    str_detect(icd_code, paste0(icd_sci,"|\\b952|\\b953"))&
      str_detect(icd_code, paste0(icd_fracture,"|\\b806"))~"SCI_Fracture",
    str_detect(icd_code, "\\b806")~"SCI_Fracture",
    str_detect(icd_code, icd_sci)&
      !str_detect(icd_code, paste0(icd_fracture,"|\\b806"))~"SCI_noFracture",
    str_detect(icd_code, icd_fracture)&
      !str_detect(icd_code, paste0(icd_sci,"|\\b952|\\b953|\\b806"))~"Spine Trauma",
    TRUE~"test"
  ))

table(mimic_patients_all$cohort_group)

## 
##   SCI_Fracture SCI_noFracture   Spine Trauma 
##            422            175           3551

MIMIC Marker Exploratory Data Analysis (EDA)

Lab Marker summaries

Note on LOINC codes. LOINC stands for Logical Observation Identifiers Names and Codes, and it is a clinical terminology standard for laboratory test and results, which allow us to now detailed information of the extracted lab test.

colnames(mimic4_lab_items) <- str_to_lower(colnames(mimic4_lab_items))

# Filter labs data based on cohort selection
mimic_labs_all <- mimic_labs_all %>%
  filter(hadm_id %in% mimic_patients_all$hadm_id)

# Generate summary statistics for lab measurement frequency by itemid
summary_labs <- mimic_labs_all %>%
  mutate(value = as.numeric(value),
         hadm_id = as.character(hadm_id)) %>% 
  filter(!is.na(value)) %>% 
  group_by(itemid, hadm_id) %>%
  summarise(time_points=n()) %>% 
  group_by(itemid)%>%
  summarise(mean_time_points = mean(time_points), 
            max_time_points = max(time_points),
            n_patients=n()) %>%
  mutate(per_patients = n_patients/length(unique(mimic_labs_all$hadm_id))) %>%
  left_join(mimic3_lab_items) %>%
  left_join(mimic4_lab_items)


# Generate and save summary table as HTML
summary_labs %>%
  .[complete.cases(.),] %>%
  arrange(desc(per_patients)) %>%
  select(itemid,loinc_code, label, fluid, category, per_patients )%>%
  kbl(digits = 2) %>%
  kable_paper() %>%
  save_kable(file = "tables/summary_labs.html")


# Cross-tabulate fluid vs. category
addmargins(table(summary_labs$fluid, summary_labs$category, useNA = "always")) %>%
  kbl() %>%
  kable_paper()

	Blood Gas	Chemistry	Hematology	NA	Sum
Ascites	0	9	13	0	22
Blood	24	104	71	0	199
Cerebrospinal Fluid (CSF)	0	3	13	0	16
Joint Fluid	0	1	11	0	12
Other Body Fluid	1	9	14	0	24
Pleural	0	8	15	0	23
Urine	0	27	20	0	47
NA	0	0	0	70	70
Sum	25	161	157	70	413

Summary

We see from the table that a lot of lab assays have been performed in very little number of patients. For consistency across datasets and proof of concept, we selected assays that are found across most patients.

Most common 20 assays

summary_labs %>%
  arrange(desc(per_patients)) %>%
  head(n=20) %>%
  kableExtra::kbl() %>%
  kable_classic()

itemid	mean_time_points	max_time_points	n_patients	per_patients	label	fluid	category	loinc_code
51221	9.681284	153	3489	0.9770372	Hematocrit	Blood	Hematology	4544-3
51301	8.166763	152	3472	0.9722767	White Blood Cells	Blood	Hematology	804-5
51222	8.159608	152	3471	0.9719966	Hemoglobin	Blood	Hematology	718-7
51248	8.124460	152	3471	0.9719966	MCH	Blood	Hematology	785-6
51249	8.126765	152	3471	0.9719966	MCHC	Blood	Hematology	786-4
51250	8.125036	152	3471	0.9719966	MCV	Blood	Hematology	787-2
51277	8.108614	152	3471	0.9719966	RDW	Blood	Hematology	788-0
51279	8.125036	152	3471	0.9719966	Red Blood Cells	Blood	Hematology	789-8
51265	8.284726	228	3470	0.9717166	Platelet Count	Blood	Hematology	777-3
50912	8.403175	189	3465	0.9703164	Creatinine	Blood	Chemistry	2160-0
51006	8.397691	189	3465	0.9703164	Urea Nitrogen	Blood	Chemistry	3094-0
50971	8.796458	325	3444	0.9644357	Potassium	Blood	Chemistry	2823-3
50902	8.428655	324	3441	0.9635956	Chloride	Blood	Chemistry	2075-0
50983	8.676547	326	3441	0.9635956	Sodium	Blood	Chemistry	2951-2
50868	8.192084	185	3436	0.9621955	Anion Gap	Blood	Chemistry	1863-0
50882	8.209546	185	3436	0.9621955	Bicarbonate	Blood	Chemistry	1963-8
50931	8.260553	186	3435	0.9619154	Glucose	Blood	Chemistry	2345-7
50960	8.240506	143	3239	0.9070288	Magnesium	Blood	Chemistry	2601-3
50893	7.756211	142	3220	0.9017082	Calcium, Total	Blood	Chemistry	2000-8
50970	7.884771	142	3211	0.8991879	Phosphate	Blood	Chemistry	2777-1

Let’s take a look at the most common markers with some spaghetti plots and density plots. We define the set of the 20 most common markers as the modeling set

# Extract top 20 most frequent lab item IDs by patient
item_id <- summary_labs %>%
  arrange(desc(per_patients)) %>%
  slice_head(n = 20) %>%
  pull(itemid)

# Define modeling dataset
mimic_modeling_set <- mimic_labs_all %>%
  filter(itemid %in% item_id) %>%
  filter(subject_id %in% mimic_patients_all$subject_id)

The time scale for MIMIC is in minutes. To have a better idea of the time interval between measures for each marker, we summarize the time component.

# Summary of average time interval (in minutes) between lab events per patient and label
time_frequency <- mimic_modeling_set %>%
  group_by(subject_id, label) %>%
  summarise(
    time_freq = mean(abs(diff(time_minutes)), na.rm = TRUE),  # Mean interval between timepoints
    .groups = "drop"
  ) %>%
  group_by(label) %>%
  summarise(
    min    = min(time_freq, na.rm = TRUE),
    Q1     = quantile(time_freq, probs = 0.25, na.rm = TRUE),
    median = median(time_freq, na.rm = TRUE),
    Q3     = quantile(time_freq, probs = 0.75, na.rm = TRUE),
    max    = max(time_freq, na.rm = TRUE),
    .groups = "drop"
  )

# Render the time frequency summary as an HTML table
time_frequency %>%
  kableExtra::kbl(digits = 2) %>%  
  kableExtra::kable_classic()

label	min	Q1	median	Q3	max
Anion Gap	55	1181.53	1477.92	2407.50	23300.64
Bicarbonate	55	1162.60	1457.75	2219.50	23315.31
Calcium, Total	55	1216.56	1472.10	2346.09	24195.43
Chloride	55	1145.00	1453.00	2181.50	22938.41
Creatinine	51	1111.99	1441.06	2169.04	23418.87
Glucose	55	1310.31	1793.50	2945.75	70767.59
Hematocrit	51	950.00	1387.47	1925.00	25840.04
Hemoglobin	51	1110.00	1443.33	2144.23	26464.14
MCH	51	1118.00	1443.60	2152.50	26464.14
MCHC	51	1118.00	1443.50	2152.50	26448.42
MCV	51	1125.50	1445.00	2176.67	26412.60
Magnesium	55	1192.14	1464.00	2243.15	24149.69
Phosphate	55	1223.29	1487.17	2324.47	24184.06
Platelet Count	51	1154.00	1460.00	2235.00	26116.24
Potassium	55	1189.21	1505.75	2351.05	24841.37
RDW	51	1120.30	1443.54	2142.93	27705.42
Red Blood Cells	51	1120.09	1443.33	2142.50	27733.32
Sodium	55	1179.35	1481.75	2340.69	24751.89
Urea Nitrogen	51	1109.19	1440.00	2135.90	24321.81
White Blood Cells	51	1125.36	1447.50	2177.50	29743.08

We work the rest of this analysis in fractions of days instead of minutes

# Plot raw lab trajectories by label, grouped by subject and plotted over days since ED arrival
modeling_set_labs_raw_plot <- mimic_modeling_set %>%
  ggplot(aes(floor(time_minutes/1440), valuenum))+
  geom_line(aes(group = subject_id))+
  facet_wrap(~ label, scales = "free")+
  xlab("Days from arrival") +
  ylab("Marker value") +
  theme_minimal()

modeling_set_labs_raw_plot

ggsave("figures/modeling_set_labs_raw.png", modeling_set_labs_raw_plot, height = 6, width = 10)

This next chunk takes the count of the number of patients with data per day

# Plot log-scaled number of subjects contributing lab measurements over time, by lab marker
modeling_set_labs_log_plot <- mimic_modeling_set %>%
  mutate(time_days = floor(time_minutes/1440)) %>%
  group_by(time_days, label, subject_id) %>% 
  summarise(mean_values = mean (valuenum, na.rm = T)) %>% 
  group_by(time_days, label) %>%
  summarise(count = n()) %>% 
  filter(time_days >= 0) %>%
  ggplot(aes(time_days, log(count)))+
    geom_line()+
    facet_wrap(~ label, scales = "free")+
    xlab("Days from arrival")+
    ylab("Number of subject (log scale)")+
    theme_minimal()

modeling_set_labs_log_plot

ggsave("figures/modeling_set_labs_log_subjects.png", modeling_set_labs_log_plot, height = 6, width = 10)

Marginal densities of the modeling set

# Plot density of lab values (valuenum) for each analyte
modeling_set_labs_density_plot <- mimic_modeling_set %>%
  ggplot(aes(valuenum))+
    geom_density()+
    facet_wrap(~ label, scales = "free")+
    xlab("Analyte value")+
    theme_minimal()

modeling_set_labs_density_plot

ggsave("figures/modeling_set_labs_density.png", modeling_set_labs_density_plot, height = 6, width = 10)

Marker data cleaning

We can see the presence of potential outliers as well as patients that have data for a long period of time.

To deal with outliers that may be indicative of wrong readings, we filter all the extreme values (unwanted data, anomalies). Since we don’t want to take potential real values away even if they are extreme, we apply a very permissive filter. we could use \(1.5*IQR\) inner fence method suggested by John Tukey (also known as the Tukey’s rule), however, it may be quite restrictive in this case, specially with skewed distributions (Seo 2006). Therefore, we apply a filter followings Tukey’s rule for each subject, but using 20% and 80% instead of the usual 25% and 75%.

The first filter we consider is to eliminate any value of 0 which makes little sense in these variables.
Then filter extreme values with quantile(prob = 0.2) - 1.5IQR as lower cut off and quantile(prob = 0.8) + 1.5IQR

# Filter lab events to retain only valid positive values and post-arrival timepoints
mimic_modeling_set <- mimic_modeling_set %>%
  filter(valuenum > 0, time_minutes >= 0)

# Apply outlier deletion within each subject-analyte trajectory
mimic_modeling_set <- mimic_modeling_set %>%
  arrange(time_minutes) %>%
  group_by(label, subject_id) %>%
  mutate(value_clean = extreme_deletion(valuenum),
         out = ifelse(is.na(value_clean), 1,0)) %>%
  filter(out == 0)

Selecting time window for analysis

Let’s see the distribution of discharge time

# Calculate length of stay in days for each subject
mimic_patients_all <- mimic_patients_all %>%
  mutate(dischtime = strptime(dischtime, format = "%Y-%m-%d %H:%M:%S"),
         time_disch_days = as.numeric(difftime(dischtime, admittime, units = "days")))

saveRDS(mimic_patients_all, file = "data/mimic_patients_all.RDS")

# Plot A: Histogram of length of stay 
los_plot_a<-ggplot(mimic_patients_all, aes(time_disch_days))+
  geom_histogram()+
  ylab("Number of subjects")+
  xlab("Length of stay (days)")+
  theme_minimal()

# Plot B: Log count of timepoints per subject by label 
los_plot_b<-mimic_modeling_set%>%
  group_by(subject_id, label)%>%
  summarise(count = n())%>%
  ggplot(aes(label, log(count)))+
    xlab(NULL)+
    ylab("Number of timepoints\nper subject (log scale)")+
    geom_boxplot()+
    coord_flip()+
    theme_minimal()

# Plot C: Raw count of timepoints per subject by label (capped at 50) 
los_plot_c<-mimic_modeling_set%>%
  group_by(subject_id, label)%>%
  summarise(count = n())%>%
  ggplot(aes(label, count))+
    xlab(NULL)+
    ylab("Number of timepoints\nper subject")+
    geom_boxplot()+
    coord_flip()+
    theme_minimal()+
    ylim(0, 50)

# Combine all three plots into a single figure
fig_los <-los_plot_a+ los_plot_b+ los_plot_c
fig_los <- fig_los+ plot_annotation(tag_levels = "a")
fig_los

ggsave("figures/modeling_set_los.png",fig_los, height = 3.5, width = 10)

Given the above data, we analyzed data no longer than 21 days from hospital arrival (3 first weeks).

# Filter the observations to the first 21 days
mimic_modeling_set <- mimic_modeling_set %>%
  mutate(time_days = time_minutes/1440)%>%
  filter(time_days <= 21)

In addition, we only considered subjects with at least 3 measures

time_mimic_labs_sum <- mimic_modeling_set %>%
  group_by(subject_id, label) %>%
  summarise(n_times=n())

mimic_labs_hadm_filter <- time_mimic_labs_sum %>%
  filter(n_times>2)

# Filter based on subjects with more than 2 timepoints
mimic_modeling_set_f <- mimic_modeling_set %>%
  filter(subject_id %in% unique(mimic_labs_hadm_filter$subject_id))

Clean Plots

## Spaghetti plot

# single patient data to show highlight
red_patient <- mimic_modeling_set_f %>%
  filter(subject_id == "15694968")

blue_patient <- mimic_modeling_set_f %>%
  filter(subject_id == "87134")

spaghetti_plot <- mimic_modeling_set_f %>%
  ggplot(aes(time_days, value_clean)) +
    geom_line(aes(group=subject_id), alpha = 0.2) +
    geom_line(data = red_patient, aes(group=subject_id), color = "firebrick1") +
    geom_line(data = blue_patient, aes(group=subject_id), color = "steelblue1") +
    facet_wrap(~label, scales = "free") +
    xlab("Days from hospital arrival") +
    ylab("Blood marker value") +
    theme_minimal()

spaghetti_plot

ggsave("figures/modeling_set_labs_clean.png", spaghetti_plot, height = 6, width = 10)

# Marginal density plot
modeling_set_labs_clean_plot <- mimic_modeling_set_f %>%
  ggplot(aes(value_clean))+
    geom_density()+
    facet_wrap(~label, scales = "free")+
    xlab("Analyte value")+
    theme_minimal()

modeling_set_labs_clean_plot

ggsave("figures/modeling_set_labs_clean_density.png", modeling_set_labs_clean_plot , height = 6, width = 10)

Final MIMIC cohort

mimic_patients_all <- mimic_patients_all %>%
  filter(subject_id %in% unique(mimic_labs_hadm_filter$subject_id))

length(unique(mimic_patients_all$subject_id))

## [1] 2615

MIMIC Demographics table

mimic_patients_all %>%
  ungroup() %>%
  select(age, gender, insurance, ethnicity, dataset, cohort_group) %>%
  tbl_summary(by = cohort_group)%>%
  add_p(test = list(all_categorical() ~ "my_fisher")) %>%
  add_q()

Characteristic	SCI_Fracture N = 382¹	SCI_noFracture N = 125¹	Spine Trauma N = 2,108¹	p-value²	q-value³
age	55 (39, 71)	58 (45, 72)	66 (44, 82)	<0.001	<0.001
gender				<0.001	<0.001
F	101 (26%)	37 (30%)	924 (44%)
M	281 (74%)	88 (70%)	1,184 (56%)
insurance				0.009	0.009
Medicaid	37 (9.7%)	13 (10%)	154 (7.3%)
Medicare	118 (31%)	48 (38%)	865 (41%)
Other	221 (58%)	62 (50%)	1,064 (50%)
Other Government	6 (1.6%)	2 (1.6%)	25 (1.2%)
ethnicity				<0.001	<0.001
ASIAN	6 (1.9%)	0 (0%)	43 (2.3%)
BLACK/AFRICAN AMERICAN	14 (4.4%)	23 (20%)	88 (4.8%)
HISPANIC/LATINO	14 (4.4%)	9 (7.9%)	77 (4.2%)
MULTI RACE/ETHNICITY	1 (0.3%)	0 (0%)	5 (0.3%)
OTHER	17 (5.4%)	5 (4.4%)	84 (4.6%)
WHITE	264 (84%)	77 (68%)	1,540 (84%)
Unknown	66	11	271
dataset				<0.001	<0.001
MIMIC-III	231 (60%)	49 (39%)	826 (39%)
MIMIC-IV	151 (40%)	76 (61%)	1,282 (61%)
¹ Median (Q1, Q3); n (%)
² Kruskal-Wallis rank sum test; Fisher’s Exact Test for Count Data with simulated p-value (based on 2000 replicates)
³ False discovery rate correction for multiple testing

MIMIC Stay characteristics

mimic_patients_all %>%
  ungroup() %>%
  select(time_disch_days, admission_location, discharge_location, n_codes,cohort_group) %>%
  mutate(n_codes = as.numeric(n_codes)) %>%
  tbl_summary(by = cohort_group) %>%
  add_p(test = list(all_categorical() ~ "my_fisher")) %>%
  add_q()

Characteristic	SCI_Fracture N = 382¹	SCI_noFracture N = 125¹	Spine Trauma N = 2,108¹	p-value²	q-value³
time_disch_days	11 (7, 19)	8 (5, 13)	7 (4, 11)	<0.001	<0.001
Unknown	1	0	0
admission_location				0.084	0.10
CLINIC REFERRAL	55 (14%)	22 (18%)	251 (12%)
EMERGENCY ROOM	301 (79%)	89 (71%)	1,730 (82%)
TRANSFER FROM HOSP	20 (5.3%)	12 (9.6%)	95 (4.5%)
TRANSFER FROM SNF	0 (0%)	0 (0%)	3 (0.1%)
WALK-IN/SELF REFERRAL	4 (1.1%)	2 (1.6%)	20 (1.0%)
Unknown	2	0	9
discharge_location				<0.001	<0.001
ACUTE HOSPITAL	4 (1.0%)	0 (0%)	9 (0.4%)
AGAINST ADVICE	0 (0%)	2 (1.6%)	11 (0.5%)
DIED	46 (12%)	6 (4.9%)	141 (6.9%)
HOME	54 (14%)	45 (37%)	668 (33%)
HOSPICE	2 (0.5%)	1 (0.8%)	19 (0.9%)
ICF	1 (0.3%)	0 (0%)	0 (0%)
LONG TERM CARE	22 (5.8%)	5 (4.1%)	88 (4.3%)
REHAB	208 (54%)	45 (37%)	528 (26%)
SHORT TERM CARE	3 (0.8%)	0 (0%)	8 (0.4%)
SKILLED NURSING FACILITY	35 (9.2%)	17 (14%)	544 (27%)
TRANSFER TO OTHER	7 (1.8%)	2 (1.6%)	29 (1.4%)
Unknown	0	2	63
n_codes	13 (9, 18)	13 (8, 21)	14 (9, 20)	0.10	0.10
¹ Median (Q1, Q3); n (%)
² Kruskal-Wallis rank sum test; Fisher’s Exact Test for Count Data with simulated p-value (based on 2000 replicates)
³ False discovery rate correction for multiple testing

MIMIC-III vs. MIMIC-IV demographics

mimic_patients_all %>%
  ungroup() %>%
  select(age, gender, insurance, ethnicity, dataset, cohort_group) %>%
  tbl_summary(by = dataset)%>%
  add_p(test = list(all_categorical() ~ "my_fisher")) %>%
  add_q()

Characteristic	MIMIC-III N = 1,106¹	MIMIC-IV N = 1,509¹	p-value²	q-value³
age	53 (35, 72)	70 (51, 84)	<0.001	<0.001
gender			<0.001	<0.001
F	366 (33%)	696 (46%)
M	740 (67%)	813 (54%)
insurance			<0.001	<0.001
Medicaid	114 (10%)	90 (6.0%)
Medicare	335 (30%)	696 (46%)
Other	624 (56%)	723 (48%)
Other Government	33 (3.0%)	0 (0%)
ethnicity			0.055	0.055
ASIAN	18 (1.8%)	31 (2.4%)
BLACK/AFRICAN AMERICAN	47 (4.8%)	78 (6.1%)
HISPANIC/LATINO	46 (4.7%)	54 (4.2%)
MULTI RACE/ETHNICITY	6 (0.6%)	0 (0%)
OTHER	47 (4.8%)	59 (4.6%)
WHITE	817 (83%)	1,064 (83%)
Unknown	125	223
cohort_group			<0.001	<0.001
SCI_Fracture	231 (21%)	151 (10%)
SCI_noFracture	49 (4.4%)	76 (5.0%)
Spine Trauma	826 (75%)	1,282 (85%)
¹ Median (Q1, Q3); n (%)
² Wilcoxon rank sum test; Fisher’s Exact Test for Count Data; Fisher’s Exact Test for Count Data with simulated p-value (based on 2000 replicates)
³ False discovery rate correction for multiple testing

MIMIC-III vs. MIMIC-IV stay

mimic_patients_all %>%
  ungroup() %>%
  select(time_disch_days, admission_location, discharge_location, n_codes,
         cohort_group, dataset) %>%
  mutate(n_codes = as.numeric(n_codes)) %>%
  tbl_summary(by = dataset) %>%
  add_p(test = list(all_categorical() ~ "my_fisher")) %>%
  add_q()

Characteristic	MIMIC-III N = 1,106¹	MIMIC-IV N = 1,509¹	p-value²	q-value³
time_disch_days	9 (6, 16)	6 (4, 10)	<0.001	<0.001
Unknown	0	1
admission_location			<0.001	<0.001
CLINIC REFERRAL	235 (21%)	93 (6.2%)
EMERGENCY ROOM	863 (78%)	1,257 (84%)
TRANSFER FROM HOSP	8 (0.7%)	119 (7.9%)
TRANSFER FROM SNF	0 (0%)	3 (0.2%)
WALK-IN/SELF REFERRAL	0 (0%)	26 (1.7%)
Unknown	0	11
discharge_location			<0.001	<0.001
ACUTE HOSPITAL	0 (0%)	13 (0.9%)
AGAINST ADVICE	7 (0.6%)	6 (0.4%)
DIED	108 (9.8%)	85 (5.9%)
HOME	320 (29%)	447 (31%)
HOSPICE	6 (0.5%)	16 (1.1%)
ICF	1 (<0.1%)	0 (0%)
LONG TERM CARE	48 (4.3%)	67 (4.6%)
REHAB	466 (42%)	315 (22%)
SHORT TERM CARE	11 (1.0%)	0 (0%)
SKILLED NURSING FACILITY	114 (10%)	482 (33%)
TRANSFER TO OTHER	25 (2.3%)	13 (0.9%)
Unknown	0	65
n_codes	11 (8, 16)	16 (10, 22)	<0.001	<0.001
cohort_group			<0.001	<0.001
SCI_Fracture	231 (21%)	151 (10%)
SCI_noFracture	49 (4.4%)	76 (5.0%)
Spine Trauma	826 (75%)	1,282 (85%)
¹ Median (Q1, Q3); n (%)
² Wilcoxon rank sum test; Fisher’s Exact Test for Count Data with simulated p-value (based on 2000 replicates)
³ False discovery rate correction for multiple testing

TRACK-SCI data

track_sci_odc<-read.csv("data/TRACKSCI/track_sci_labs_odc.csv", check.names = F)

Process data

## clean lab values
track_sci_odc<-track_sci_odc%>%
  pivot_longer(cols = all_of(colnames(track_sci_odc)[7:25]), names_to = "lab_name", 
               values_drop_na = T)%>%
  arrange(time_days)%>%
  mutate(StudyID = factor(StudyID),
         subject_id_num = as.numeric(StudyID))%>%
  group_by(lab_name, StudyID)%>%
  mutate(value_clean = extreme_deletion(value))

track_static_df<-track_sci_odc%>%
  ungroup()%>%
  select(subject_id_num, age, gender, latest_AIS_at_hospital)%>%
  group_by(subject_id_num)%>%
  summarise_all(.funs = unique)

TRACK table

track_static_df%>%
  select(age, gender, latest_AIS_at_hospital)%>%
  tbl_summary()

Characteristic	N = 137¹
age	58 (40, 69)
gender
Female	40 (29%)
Male	97 (71%)
latest_AIS_at_hospital
A	25 (19%)
B	7 (5.4%)
C	20 (15%)
D	75 (58%)
E	3 (2.3%)
Unknown	7
¹ Median (Q1, Q3); n (%)

TRACK-SCI spaghetti plots

track_spaghetti_plot<-track_sci_odc%>%
  ggplot(aes(time_days, value_clean, group = StudyID))+
    geom_line()+
    facet_wrap(~lab_name, scales = "free")+
    ylab("Marker value")+xlab("Days from arrival")+
    theme_minimal()

track_spaghetti_plot

ggsave("figures/track_modeling_set.png", track_spaghetti_plot, 
       height = 6, width = 10)

Blood Trajectory Modeling in MIMIC

mimic_modeling_set_f <- mimic_modeling_set_f %>%
  ungroup()%>%
  filter(subject_id %in% unique(mimic_patients_all$subject_id)) %>%
  mutate(subject_id_num=as.numeric(as.factor(subject_id)))

saveRDS(mimic_modeling_set_f, "data/mimic_modeling_set_f.Rds")

lab_names<-unique(mimic_modeling_set_f$label)

hematology_var<-summary_labs%>%
  filter(category == "Hematology", fluid =="Blood",label %in%lab_names)%>%
  .$label

chemistry_var<-summary_labs%>%
  filter(category == "Chemistry", fluid =="Blood",label %in% lab_names)%>%
  .$label

LCGA: Initial exploratory modeling

The following script runs the modeling for the modeling set of markers. Given that this is exploratory to have an initial sense of the trajectories, we will use LCGA (no random effect). This process is computationally costly. The function helps with running all the code for a single marker. It iterates through different models for the selection of number of classes and polynomial order. For each marker would perform a linear search for the number of components (ng = {1, 2, 3, 4, 5}) and a linear search for the polynomial order (1, 2, 3, 4).

The results for each model are saved in the ./models/ folder as .Rds files.

Note that the data for each model is not saved to prevent the data to be accessible through the .Rds files. These files contain all the elements of the fitted models.

Not run during knitting

mimic_modeling_set_f<-readRDS("data/mimic_modeling_set_f.Rds")
modeling_set_list<-split(mimic_modeling_set_f,mimic_modeling_set_f$label)

## Iterate through all the markers by parallelyzing
cl<-makeCluster(11)
model_files<-list.files("models/", full.names = T)
clusterExport(cl, c("my_LCGA", "modeling_set_list", "model_files"))
clusterEvalQ(cl, library(lcmm))

parLapply(cl, 1:20, fun = function(a){
  my_LCGA(modeling_set_list[[a]])
})

stopCluster(cl)

Read the files and process them

model_files<-list.files("models/", full.names = T)
models_list<-list()

## Read all the models in a list of lists
for (l in lab_names){
  temp_list<-list()
  for (f in model_files){
    if (str_split(f, "_", simplify = T)[,2]==l){
      temp_list[[f]]<-readRDS(f)
    }
  }

  models_list[[l]]<-temp_list
}

saveRDS(models_list, "models/models_list.Rds")

The following chunks extract the model metrics and save the LCGA plots

models_list<-readRDS("data/models_list.Rds")

model_selection_list<-list()
for (l in lab_names){
  model_selection_list[[l]]<-model_metrics(l, models_list)
}

LCGA_plots<-lapply(model_selection_list, function(x){
  ggsave(plot = x$fig, paste0("figures/LCGA fit plots/LCGA_", x$lab_name, ".png"), 
         width = 8, height = 2.6)
  x$fig
})

# saveRDS(LCGA_plots, "figures/LCGA_plots.Rds")

LCGA summaries

Summary Plots

# Summary plot for the hematolgy markers
LCGA_fig_hem<-wrap_plots(LCGA_plots[hematology_var])+
  plot_layout(guides = "collect",ncol = 2)+
  plot_annotation(tag_levels = "a")


ggsave(plot = LCGA_fig_hem, "figures/LCGA_fig_hem.png", width = 12, height = 12)

# Summary plot for the chemistry markers
LCGA_fig_chem<-wrap_plots(LCGA_plots[chemistry_var])+
  plot_layout(guides = "collect",ncol = 2)+
  plot_annotation(tag_levels = "a")

ggsave(plot = LCGA_fig_chem, "figures/LCGA_fig_chem.png", width = 12, height = 14)

Summary Tables

lapply(model_selection_list, function(x){
  x$table%>%
    arrange(G, linktype,npm, poly)%>%
    select(G, conv, linktype,npm, poly, BIC, ICL, APPA, `%class1`, `%class2`,
           `%class3`, `%class4`, `%class5`)%>%
    rowwise()%>%
    mutate(across(.cols = c("BIC", "ICL", "APPA", "%class1", "%class2",
           "%class3", "%class4", "%class5"), .fns = function(x){
             if(!conv%in%c(1,2)) NA else x
           }))%>%
    select(-conv)%>%
    kableExtra::kbl(row.names = F,digits = 2)%>%
    kable_styling(full_width = F)%>%
    kable_classic()%>%
    kableExtra::save_kable(file = paste0("tables/LCGA fit tables/LCGA_", 
                                         x$lab_name, ".html"))
})

GMM: model search

This section reads the selected model parameters from the LCGA exploration and fit selected GMM models. As before, this is computationally expensive and was done through paralellization.

Not run during knitting

GMM_param<-read.csv("GMM_param.csv")
colnames(GMM_param)[1]<-"analyte"

model_files<-list.files("models_GMM/", full.names = T)
GMM_param<-GMM_param%>%
  arrange(k)

cl<-makeCluster(12)
clusterExport(cl, c("modeling_set_list", "GMM_param", "my_GMM","model_files"))
clusterEvalQ(cl, library(lcmm))
clusterEvalQ(cl, library(splines))

## M1 models
model_files<-list.files("models_GMM/", full.names = T)
clusterExport(cl, c("model_files"))
parLapply(cl, 1:nrow(GMM_param%>%filter(k==1)), function(i){
  my_GMM(GMM_param[i,1], GMM_param[i,3], GMM_param[i,2],GMM_param[i,4], GMM_param[i,5], .nproc = 2)
})

## All other models
model_files<-list.files("models_GMM/", full.names = T)
clusterExport(cl, c("model_files"))
parLapply(cl, 1:nrow(GMM_param), function(i){
  my_GMM(GMM_param[i,1], GMM_param[i,3], GMM_param[i,2],GMM_param[i,4],GMM_param[i,5], .nproc = 2)
})

stopCluster(cl)

Read the files and process them

model_files<-list.files("models_GMM/", full.names = T)

## Read all the models in a list of lists
model_GMM_list<-lapply(model_files, readRDS)
names(model_GMM_list) <- model_files

The following chunk extracts the summary metrics for model selection

model_selection_GMM_list<-mapply(function(x,i){
    summary_metrics(x, i, .modeltype = 5, .analyte = 3, .linktype = 4)
  }, 
  model_GMM_list, 
  names(model_GMM_list), SIMPLIFY = F)

model_selection_GMM_df<-do.call(rbind, model_selection_GMM_list)
model_selection_GMM_list <- split(model_selection_GMM_df, model_selection_GMM_df$analyte)

GMM_plots<-lapply(model_selection_GMM_list, function(x){
    table_m_selection<-x%>%
      as.data.frame(.)%>%
      mutate(linktype = ifelse(linktype == "3-quant-splines", "spline", linktype),
             linktype = factor(linktype, levels = c("linear", "beta", "spline")))
    
    table_m_selection_plot<-table_m_selection%>%
      filter(conv %in% c(1,2))%>%
      select(G, BIC, ICL, APPA, poly, linktype)%>%
      pivot_longer(-c(G, poly, linktype))%>%
      mutate(name = factor(name, levels = c("BIC", "ICL", "APPA")))
      
   figure<-ggplot(table_m_selection_plot, aes(G, value, color = poly,
                                       shape = linktype, linetype = linktype))+
      geom_point()+
      geom_line()+
      facet_wrap(~name, scales = "free")+
      xlab("Number of classes")+
      ylab(NULL)+
      theme_minimal()+
      ggtitle(unique(table_m_selection$analyte))
   
   ggsave(plot = figure, paste0("figures/GMM fit plots/GMM_", unique(table_m_selection$analyte), ".png"), width = 8, height = 2.6)
   
   figure
})

GMM summaries

Summary Plots

# Summary hematology plots for GMMs
GMM_fig_hem<-wrap_plots(GMM_plots[hematology_var])+
  plot_layout(guides = "collect",ncol = 2)+
  plot_annotation(tag_levels = "a")

ggsave(plot = GMM_fig_hem, "figures/GMM_fig_hem.png", width = 12, height = 12)

# Summary chemestry plots for GMMs
GMM_fig_chem<-wrap_plots(GMM_plots[chemistry_var])+
  plot_layout(guides = "collect",ncol = 2)+
  plot_annotation(tag_levels = "a")

ggsave(plot = GMM_fig_chem, "figures/GMM_fig_chem.png", width = 12, height = 14)

Summary Tables

model_selection_GMM_df%>%
    arrange(analyte, G, linktype,npm, poly)%>%
    select(analyte, G, conv, linktype,npm, poly, BIC, ICL, APPA, `%class1`, `%class2`,
           `%class3`, `%class4`, `%class5`)%>%
    rowwise()%>%
    mutate(across(.cols = c("BIC", "ICL", "APPA", "%class1", "%class2",
           "%class3", "%class4", "%class5"), .fns = function(x){
             if(!conv%in%c(1,2)) NA else x
           }))%>%
    select(-conv)%>%
    kableExtra::kbl(row.names = F,digits = 2)%>%
    kable_styling(full_width = F)%>%
    kable_classic()%>%
    kableExtra::save_kable(file = "tables/GMM fit tables/GMM_table.html")

GMM: final models

files_GMM_selected<-list.files("models_GMM_selected/", full.names = T)

GMM_list<-lapply(files_GMM_selected, readRDS)
names(GMM_list)<-files_GMM_selected

model_final_GMM_list<-mapply(function(x, i){summary_metrics(x,i)}, 
                             GMM_list, names(GMM_list), SIMPLIFY = F)

model_final_GMM_df<-do.call(rbind, model_final_GMM_list)

model_final_GMM_df%>%
    arrange(analyte, G, linktype,npm, poly)%>%
    select(analyte, G, conv, linktype,npm, poly, BIC, ICL, APPA, `%class1`, `%class2`,
           `%class3`, `%class4`, `%class5`)%>%
    rowwise()%>%
    mutate(across(.cols = c("BIC", "ICL", "APPA", "%class1", "%class2",
           "%class3", "%class4", "%class5"), .fns = function(x){
             if(!conv%in%c(1,2)) NA else x
           }))%>%
    select(-conv)%>%
    kableExtra::kbl(row.names = F,digits = 2)%>%
    kable_styling(full_width = F)%>%
    kable_classic()%>%
    kableExtra::save_kable(file = "tables/GMM fit tables/final_GMM_table.html")

GMM_list<-mapply(function(x, i){
  d <- str_split(i, "_", simplify = T)[,6]
  k <- str_sub(d, 2,2)
  d <- str_sub(d, 4,4)
  link <- str_split(i, "_", simplify = T)[,5]
  
  if (d ==1){
    model_form<-paste0("value_clean ~ time_days")
    random<-paste0("~ time_days")
  }else{
    model_form<-paste0("value_clean ~ splines::ns(time_days,",d,")")
    random<-paste0("~ splines::ns(time_days,",d,")")
  }
  
  x$call$fixed<-as.formula(model_form)
  x$call$random<-as.formula(random)
  x$call$mixture<-as.formula(random)
  x$call$link<-link
  x$call$ng<-as.numeric(k)
  
  return(x)
  
}, GMM_list, names(GMM_list), SIMPLIFY = F)

Plotting trajectories

new_dataset<-data.frame(time_days = seq(0,21,length.out = 100))

GMM_plots<-mapply(function(x, i){
  analyte<-str_split(i, "_", simplify = T)[,4]
  k <- str_split(i, "_", simplify = T)[,6]
  k <- str_sub(k, 2,2)
  
  plot_GMM(x, analyte,k)
}, GMM_list, names(GMM_list), SIMPLIFY = F)

GMM_fig_traj<-wrap_plots(GMM_plots)+plot_layout(ncol = 4)

GMM_fig_traj

ggsave(plot = GMM_fig_traj, "figures/GMM_fig_traj.png", width = 22, height = 16, dpi = 300)

Trajectory description

Preparation of the class probability

class_GMM_list<-mapply(function(x, i){
  pprob<-x$pprob
  if(ncol(pprob) == 4){
    pprob$prob3<-NA
  }else if(ncol(pprob) ==3){
    pprob$prob2<-NA
    pprob$prob3<-NA
  }
  pprob$analyte<-str_split(i, "_", simplify = T)[,4]
  
  return(pprob)
}, GMM_list, names(GMM_list), SIMPLIFY = F)

class_GMM_df<-do.call(rbind, class_GMM_list)

subject_id_df<-mimic_modeling_set_f%>%
  select(subject_id, subject_id_num)%>%
  group_by(subject_id)%>%
  summarise(subject_id_num = unique(subject_id_num))

class_GMM_df<-class_GMM_df%>%
  left_join(subject_id_df)%>%
  left_join(mimic_patients_all)%>%
  filter(!analyte %in%c("Bicarbonate", "Calcium, Total", "Creatinine"))

Probability assignment tables

class_GMM_df%>%
  group_by(analyte, class)%>%
  summarise(prob1 = mean(prob1),
            prob2 = mean(prob2),
            prob3 = mean(prob3, na.rm = T),
            count = n())

## # A tibble: 41 × 6
## # Groups:   analyte [17]
##    analyte    class   prob1  prob2   prob3 count
##    <chr>      <int>   <dbl>  <dbl>   <dbl> <int>
##  1 Anion Gap      1 0.789   0.211  NaN        52
##  2 Anion Gap      2 0.0154  0.985  NaN      2548
##  3 Chloride       1 0.874   0.126  NaN        22
##  4 Chloride       2 0.00282 0.997  NaN      2578
##  5 Glucose        1 0.767   0.233  NaN       272
##  6 Glucose        2 0.0753  0.925  NaN      2328
##  7 Hematocrit     1 0.821   0.0672   0.111   834
##  8 Hematocrit     2 0.0654  0.753    0.181   353
##  9 Hematocrit     3 0.0647  0.0982   0.837  1425
## 10 Hemoglobin     1 0.801   0.0529   0.146   696
## # ℹ 31 more rows

Tables of univariate analysis between classes per marker

class_GMM_df<-class_GMM_df%>%
  mutate(n_codes = as.numeric(n_codes))

tables_p_test<-lapply(unique(class_GMM_df$analyte), function(x){
  
    table_df<-class_GMM_df%>%
      filter(analyte == x)%>%
      select(class, age, gender,ethnicity ,cohort_group, time_disch_days,
             hospital_expire_flag, n_codes)%>%
      mutate(class = as.character(class))%>%
      tbl_summary(by=class)%>%
      add_p(test = list(all_continuous()~"oneway.test",all_categorical() ~ "my_fisher"),
            pvalue_fun = function (x) round(x,4))%>%
      add_q()
    
    table_df%>%
      as_kable()%>%
      kable_paper()%>%
      save_kable(file = paste("tables/class_diff_", x,".html"))
    
    table_df<-table_df%>%
      as_tibble()%>%
      filter(!is.na(`**p-value**`))%>%
      rename(p_value = `**p-value**`,
             q_value = `**q-value**`, variable = `**Characteristic**`)%>%
      mutate(analyte = x)
    
    return(table_df)
    
})

tables_p_test_df<-bind_rows(tables_p_test)

Heatmap plot

tables_p_test_df<-tables_p_test_df%>%
  mutate(q_value = p.adjust(tables_p_test_df$p_value, method = "fdr"),
         variable = factor(variable, levels = c("age", "gender", "ethnicity",
                                                "cohort_group", 
                                                "hospital_expire_flag","n_codes",
                                                "time_disch_days")))

heatmap_plot<-ggplot(tables_p_test_df, aes(analyte, variable, fill = q_value))+
  geom_tile()+
  geom_tile(data = tables_p_test_df %>%
              filter(q_value<0.05), color = "red", size = 0.7)+
  scale_fill_gradientn(colours = c("yellow1", "steelblue1", "steelblue4"),
                       values =  c(0,0.4,1))+
  theme_minimal(base_size = 22)+
  scale_y_discrete(position = "left", labels = c("Age","Sex", "Ethnicity",
                                                "Has SCI", 
                                                "In-hospital mortality", 
                                                "#ICD diagnostics","Length of Stay"))+
  scale_x_discrete(position = "top")+
  theme(axis.text.x.top = element_text(angle = 45, vjust = 0, hjust = 0),
        plot.margin = unit(c(-10,0,0,0), units = "mm"))+
  xlab(NULL)+ylab(NULL)+
  labs(fill = "q value")

heatmap_plot

ggsave("figures/heatmap summary cluster analysis.png", width = 10, height = 2.6)

Trajectory figure

trajectory_figure<-wrap_elements(GMM_fig_traj)+
  wrap_elements(heatmap_plot)+
  plot_layout(nrow = 2, heights = c(5,1))+plot_annotation(tag_levels = "a") &
  theme(plot.tag = element_text(size = 24))

ggsave(plot = trajectory_figure,"figures/trajectory_figure.png", width = 20, height = 26)

ML Experiments

Prediction of trajectories

This section extracts the posterior probability for the trajectories of each blood marker and prepares the data for ML experiment

nrep <- 25

Exp I and II

Not run on the time of knitting

for (cutoff in c(1,3,7,14,21)){
  
  cat("Exp I, cutoff:", cutoff, "\n")
  mimic_modeling_set_cut<-mimic_modeling_set_f%>%
    filter(time_days<=cutoff)
  modeling_set_cut_list<-split(mimic_modeling_set_cut,mimic_modeling_set_cut$label)
  
  predict_list<-list()
  for (i in 1:length(GMM_list)){
    print(i)
    model<-GMM_list[[i]]
    analyte<-str_split(names(GMM_list)[i], "_", simplify = T)[,4]
    pre_data<-modeling_set_cut_list[[analyte]]%>%
      select(subject_id_num, value_clean, time_days)
    
    if(model$ng == 1){
      next()
    }
  
    pprob<-predictClass(model, pre_data, "subject_id_num")
    
    if(ncol(pprob) == 4){
      pprob$prob3<-NA
    }else if(ncol(pprob) ==3){
      pprob$prob2<-NA
      pprob$prob3<-NA
    }
    pprob$analyte<-analyte
    
    predict_list[[i]]<-pprob
  }
  
  saveRDS(predict_list, 
          file = paste0("prediction_experiments/predict_list_taskI_II_",cutoff,
                        "days.Rds"))
}

APPA for each marker over the time window cutoff

APPA_list<-list()

for (c in c(1,3,7,14,21)){
  predict_list<-readRDS(paste0("prediction_experiments/predict_list_taskI_II_",c,"days.Rds"))

  APPA_list[[c]]<-bind_rows(predict_list)%>%
    group_by(analyte, class)%>%
    mutate(time_window = c, count = n())
}

APPA_list_df<-bind_rows(APPA_list)%>%
  group_by(analyte, class, time_window, subject_id_num)%>%
  mutate(APPA = max(prob1, prob2, prob3, na.rm = T),
         time_window_lab = case_when(
           time_window == 1 ~"<= 1 days",
           time_window == 3 ~"<= 3 days",
           time_window == 7 ~"<= 17 days",
           time_window == 14 ~"<= 14 days",
           time_window == 21 ~"<= 21 days"
         ))%>%
  filter(!is.na(class))

ggplot(APPA_list_df, aes(time_window, APPA, 
                         color = as.factor(class)))+
  stat_summary(fun = mean, geom = "line")+
  stat_summary(fun = mean, geom = "point")+
  stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.3)+
  # geom_point()+
  facet_wrap(~analyte)+
  ylim(0,1)+
  xlab("Time window prediction cutoff (<= x days)")+
  ylab("APPA ± SE")+
  scale_x_continuous(breaks = c(1, 3, 7, 14, 21))+
  theme_minimal()+
  labs(color = "class")+
  theme(legend.position = c(0.7,0.1), legend.direction = "horizontal")

ggsave("figures/APPA vs time.png", width = 7, height = 4.5)

mean(APPA_list_df$APPA)

## [1] 0.9340917

sd(APPA_list_df$APPA)

## [1] 0.121513

Exp III

track_minimal_list<-track_sci_odc%>%
  arrange(lab_name)%>%
  split(.$lab_name)

Not run on the time of knitting

for (cutoff in c(1,3,7,14,21)){
  
  cat("Exp III, cutoff:", cutoff, "\n")
  track_modeling_set_cut<-track_sci_odc%>%
    filter(time_days<=cutoff)
  modeling_set_cut_list<-split(track_modeling_set_cut,track_modeling_set_cut$lab_name)
  
  predict_list<-list()
  for (i in 1:length(GMM_list)){
    print(i)
    model<-GMM_list[[i]]
    analyte<-str_split(names(GMM_list)[i], "_", simplify = T)[,4]
    pre_data<-as.data.frame(modeling_set_cut_list[[analyte]])
    
    if(nrow(pre_data) == 0){
      next()
    }
    
    if(model$ng == 1){
      next()
    }
  
    pprob<-predictClass(model, pre_data, "subject_id_num")
    
    if(ncol(pprob) == 4){
      pprob$prob3<-NA
    }else if(ncol(pprob) ==3){
      pprob$prob2<-NA
      pprob$prob3<-NA
    }
    pprob$analyte<-analyte
    
    predict_list[[i]]<-pprob
  }

  
  saveRDS(predict_list, 
          file = paste0("prediction_experiments/predict_list_taskIII_",cutoff,
                        "days.Rds"))
}

Dynamic posterior probability of assignment on TRACK-SCI data

APPA_list_track<-list()

for (c in c(1,3,7,14,21)){
  predict_list<-readRDS(paste0("prediction_experiments/predict_list_taskIII_",c,"days.Rds"))

  APPA_list_track[[c]]<-bind_rows(predict_list)%>%
    group_by(analyte, class)%>%
    mutate(time_window = c, count = n())
}

APPA_list_track_df<-bind_rows(APPA_list_track)%>%
  group_by(analyte, class, time_window, subject_id_num)%>%
  mutate(APPA = max(prob1, prob2, prob3, na.rm = T),
         time_window_lab = case_when(
           time_window == 1 ~"<= 1 days",
           time_window == 3 ~"<= 3 days",
           time_window == 7 ~"<= 17 days",
           time_window == 14 ~"<= 14 days",
           time_window == 21 ~"<= 21 days"
         ))%>%
  filter(!is.na(class))

ggplot(APPA_list_track_df, aes(time_window, APPA, 
                         color = as.factor(class)))+
  stat_summary(fun = mean, geom = "line")+
  stat_summary(fun = mean, geom = "point")+
  stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.3)+
  # geom_point()+
  facet_wrap(~analyte)+
  ylim(0,1)+
  xlab("Time window prediction cutoff (<= x days)")+
  ylab("APPA ± SE")+
  scale_x_continuous(breaks = c(1, 3, 7, 14, 21))+
  theme_minimal()+
  labs(color = "class")+
  theme(legend.position = c(0.5,0.05), legend.direction = "horizontal")

ggsave("figures/APPA vs time track.png", width = 7, height = 4.5)

Experiment I

Blood trajectory feature set

# Run mortality prediction experiments for different time cutoffs
# This may take time depending on model complexity and number of repetitions
exp_I_list <- list()

for (cutoff in c(1, 3, 7, 14, 21)) {
  
  cat("Exp I, cutoff:", cutoff, "\n")
  
  # Load predictions for a given cutoff
  predict_list <- readRDS(paste0("prediction_experiments/predict_list_taskI_II_", cutoff, "days.Rds"))
  
  # Combine and enrich prediction data
  predict_df <- do.call(rbind, predict_list) %>%
    left_join(subject_id_df) %>%
    left_join(mimic_patients_all) %>%
    filter(!analyte %in% c("Bicarbonate", "Calcium, Total", "Creatinine")) %>%
    select(prob1, prob2, prob3, analyte, subject_id, hospital_expire_flag) %>%
    mutate(is_dead = ifelse(hospital_expire_flag, "yes", "no")) %>%
    pivot_wider(
      id_cols = c(subject_id, is_dead),
      names_from = analyte,
      values_from = c(prob1, prob2, prob3)
    )
  
  # Drop columns with all missing values
  predict_df <- predict_df %>%
    select(-all_of(names(which(sapply(predict_df, function(x) mean(is.na(x))) == 1))))
  
  # Mean impute remaining missing values
  predict_df[3:ncol(predict_df)] <- lapply(predict_df[3:ncol(predict_df)], function(x) {
    x[is.na(x)] <- mean(x, na.rm = TRUE)
    return(x)
  })
  
  # Determine class counts
  n_yes <- table(predict_df$is_dead)[["yes"]]
  n_no  <- table(predict_df$is_dead)[["no"]]
  
  # Run predictive model experiment
  exp_I_list[[as.character(cutoff)]] <- run_experiment(
    predict_df,
    target.name = "is_dead",
    n.yes.train = n_yes * 0.8,
    n.no.train  = n_no * 0.8,
    resampling = F,
    reps = nrep,
    method = "glmnet"
  )
}

# Save the result of all experiments
saveRDS(exp_I_list, paste0("prediction_experiments/exp_I_list_no_balance.Rds"))

Blood trajectory + summary stats feature set

Not run during knitting

exp_I_both_list <- list()

for (cutoff in c(1,3, 7, 14 ,21)){
  cat("Exp I raw, cutoff:", cutoff, "\n")
  
  predict_list <- readRDS(paste0("prediction_experiments/predict_list_taskI_II_",
                                 cutoff,
                                 "days.Rds"))
  
  predict_traj_df <- do.call(rbind,predict_list) %>%
    left_join(subject_id_df) %>%
    left_join(mimic_patients_all) %>%
    filter(!analyte %in% c("Bicarbonate", "Calcium, Total", "Creatinine")) %>%
    select(prob1, prob2, prob3, analyte, subject_id, hospital_expire_flag) %>%
    mutate(is_dead = ifelse(hospital_expire_flag, "yes", "no")) %>%
    pivot_wider(id_cols = c(subject_id, is_dead),
                names_from = analyte, values_from = c(prob1, prob2, prob3))
  
  predict_traj_df <- predict_traj_df %>%
    select(-all_of(names(which(sapply(predict_traj_df, 
                                      function(x) mean(is.na(x)))==1))))
  
  predict_raw_df <- mimic_modeling_set_f %>%
    filter(time_days <= cutoff) %>%
    select(subject_id,label, value_clean) %>%
    group_by(subject_id, label) %>%
    summarise(mean = mean(value_clean, na.rm = T),
              sd = sd(value_clean, na.rm = T),
              max = max(value_clean, na.rm = T),
              min = min(value_clean, na.rm = T)) %>%
    pivot_wider(id_cols = subject_id, names_from = label,
                values_from = c(mean, sd, max, min)) %>%
    left_join(mimic_patients_all %>%
                select(subject_id, hospital_expire_flag)) %>%
    mutate(is_dead = ifelse(hospital_expire_flag == 1, "yes", "no")) %>%
    relocate(is_dead, .after = subject_id) %>%
    select(-hospital_expire_flag)
  
  
  predict_df <- predict_traj_df %>%
    full_join(predict_raw_df)
  
  # Mean impute remaining missing values
  predict_df[3:ncol(predict_df)] <- lapply(predict_df[3:ncol(predict_df)], function(x) {
    x[is.na(x)] <- mean(x, na.rm = TRUE)
    return(x)
  })
  
 n_yes <- table(predict_df$is_dead)[["yes"]]
 n_no <- table(predict_df$is_dead)[["no"]]
  
 exp_I_both_list[[as.character(cutoff)]] <- run_experiment(
  predict_df, "is_dead", 
  n.yes.train = n_yes*0.8, 
  n.no.train = n_no*0.8,
  resampling = F,
  reps = nrep,
  method = "glmnet")
}

saveRDS(exp_I_both_list, paste0("prediction_experiments/exp_I_both_list_no_balance.RDS"))

Blood trajectory + summary stats + BL feature set

Not run during knitting

exp_I_add_list <- list()

for (cutoff in c(1, 3, 7, 14, 21)){
  
  cat("Exp I, cutoff:", cutoff, "\n")
  predict_list <- readRDS(paste0("prediction_experiments/predict_list_taskI_II_", cutoff,
                               "days.Rds"))
  
  predict_traj_df <- do.call(rbind,predict_list) %>%
    left_join(subject_id_df) %>%
    left_join(mimic_patients_all) %>%
    filter(!analyte %in% c("Bicarbonate", "Calcium, Total", "Creatinine")) %>%
    select(prob1, prob2, prob3, analyte, subject_id, hospital_expire_flag) %>%
    mutate(is_dead = ifelse(hospital_expire_flag, "yes", "no"))%>%
    pivot_wider(id_cols =c(subject_id, is_dead),
                names_from = analyte,
                values_from = c(prob1, prob2, prob3))
  
  predict_traj_df <- predict_traj_df %>%
    select(-all_of(names(which(sapply(predict_traj_df, 
                                      function(x) mean(is.na(x)))==1))))
  
  predict_raw_df <- mimic_modeling_set_f %>%
    filter(time_days <= cutoff) %>%
    select(subject_id,label, value_clean) %>%
    group_by(subject_id, label) %>%
    summarise(mean = mean(value_clean, na.rm = T),
              sd = sd(value_clean, na.rm = T),
              max = max(value_clean, na.rm = T),
              min = min(value_clean, na.rm = T)) %>%
    pivot_wider(id_cols =subject_id,
                names_from = label,
                values_from = c(mean, sd, max, min))
  
  predict_df <- predict_traj_df %>%
    full_join(predict_raw_df)
  
  # Mean impute remaining missing values
  predict_df[3:ncol(predict_df)] <- lapply(predict_df[3:ncol(predict_df)], function(x) {
    x[is.na(x)] <- mean(x, na.rm = TRUE)
    return(x)
  })
  
  predict_df <- predict_df %>%
    left_join(mimic_patients_all %>% 
                select(subject_id, cohort_group, age, gender, insurance, ethnicity))%>%
    filter(complete.cases(.))
  
  n_yes <- table(predict_df$is_dead)[2]
  n_no <- table(predict_df$is_dead)[1]
  
  exp_I_add_list[[as.character(cutoff)]] <- run_experiment(
   predict_df, "is_dead", 
   n.yes.train = n_yes*0.8, 
   n.no.train = n_no*0.8,
   resampling = F,
   reps = nrep,
   method = "glmnet")
}

saveRDS(exp_I_add_list, paste0("prediction_experiments/exp_I_add_list_no_balance.RDS"))

Summary performance

## blood only
# Load results of prediction experiments
exp_I_list <- readRDS(paste0("prediction_experiments/exp_I_list_no_balance.RDS"))

# Drop any NULL entries (failed experiments)
exp_I_list <- exp_I_list[which(sapply(exp_I_list, function(x) !is.null(x)))]
exp_I_per_df <- experiment_performance(exp_I_list, nrep)

## blood + summary stats
# Load results of prediction experiments
exp_I_both_list <- readRDS(paste0("prediction_experiments/exp_I_both_list_no_balance.RDS"))

# Drop any NULL entries (failed experiments)
exp_I_both_list <- exp_I_both_list[which(sapply(exp_I_both_list, function(x) !is.null(x)))]

# Extract and reshape performance metrics across all cutoff experiments
exp_I_both_per_df <- experiment_performance(exp_I_both_list, nrep)

## blood + summary stats +bl
# Load results of prediction experiments
exp_I_add_list <- readRDS(paste0("prediction_experiments/exp_I_add_list_no_balance.RDS"))

# Drop any NULL entries (failed experiments)
exp_I_add_list <- exp_I_add_list[which(sapply(exp_I_add_list, function(x) !is.null(x)))]
exp_I_add_per_df <- experiment_performance(exp_I_add_list, nrep)

exp_I_per_df$feature_list <- "Traj. PPA"
exp_I_both_per_df$feature_list <- "Traj. PPA + Sum. stats"
exp_I_add_per_df$feature_list <- "Traj. PPA + Sum. stats + BL"

exp_I_df <- bind_rows(exp_I_per_df, exp_I_both_per_df, exp_I_add_per_df)

saveRDS(exp_I_df, paste0("prediction_experiments/exp_I_df.RDS"))

Hypothesis testing

## Across feature list
f_result_featureList_I <- list()

for (c in c(1, 3, 7, 14, 21)){
  x <- exp_I_df %>%
    filter(cutoff == c) %>%
    group_by(feature_list) %>%
    mutate(block = 1:n())
  
  f_result <- friedman.test(x$pr_auc, x$feature_list, x$block)
  
  f_result_featureList_I[[c]] <- data.frame(
    cutoff = c,
    chi_squared = f_result$statistic,
    p_value = sprintf("%.5f", f_result$p.value)
  )
}

f_result_featureList_I_df <- bind_rows(f_result_featureList_I)

## Across time for each feature list
f_result_cutoff_I <- list()

for (f in unique(exp_I_df$feature_list)){
  x <- exp_I_df %>% 
    filter(feature_list == f) %>%
    group_by(cutoff) %>%
    mutate(block = 1:n())
  
  f_result <- friedman.test(x$pr_auc, x$cutoff, x$block)
  
  f_result_cutoff_I[[f]] <- data.frame(
    feature_list = f,
    chi_squared = f_result$statistic,
    p_value = sprintf("%.5f", f_result$p.value)
  )
}

f_result_cutoff_I_df <- bind_rows(f_result_cutoff_I)

kable(f_result_featureList_I_df, caption = "Friedman Test Results by Cutoff") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

Friedman Test Results by Cutoff
	cutoff	chi_squared
Friedman chi-squared…1	1	49.24
Friedman chi-squared…2	3	61.00
Friedman chi-squared…3	7	48.16
Friedman chi-squared…4	14	71.08
Friedman chi-squared…5	21	69.12

kable(f_result_cutoff_I_df, caption = "Friedman Test Results by Feature List") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

Friedman Test Results by Feature List
	feature_list	chi_squared
Friedman chi-squared…1	Traj. PPA	91.248
Friedman chi-squared…2	Traj. PPA + Sum. stats	136.160
Friedman chi-squared…3	Traj. PPA + Sum. stats + BL	86.960

Figures Exp I

PR-AUC

# Out-train performance figure PR-AUC
task_I_out_train_pr_plot <- exp_I_df %>%
  filter(per_type == "out-train") %>%
  mutate(cutoff = factor(cutoff, levels = c(1, 3, 7, 14, 21),
                         labels = c("<= 1 days", "<= 3 days",
                                    "<= 7 days","<= 14 days","<= 21 days"))) %>%
  ggplot(aes(feature_list, pr_auc, fill = feature_list))+
  geom_boxplot(linewidth = 0.1)+
  geom_hline(aes(yintercept = mean(pr_BL)), color = "red", linetype = "dashed")+
  facet_grid(~cutoff)+
  ylim(0,1)+
  ylab("PR-AUC (out-train)")+
  theme_bw()+
  theme(axis.text.x = element_blank(), axis.title.x = element_blank(),
        legend.position = "bottom", legend.title = element_blank())

task_I_out_train_pr_plot

# In-train performance figure PR-AUC
task_I_in_train_pr_plot <- exp_I_df %>%
  filter(per_type == "in-train") %>%
  mutate(cutoff = factor(cutoff, levels = c(1, 3, 7, 14, 21),
                         labels = c("<= 1 days", "<= 3 days",
                                    "<= 7 days","<= 14 days","<= 21 days"))) %>%
  ggplot(aes(feature_list, pr_auc, fill = feature_list))+
  geom_boxplot(linewidth = 0.1)+
  geom_hline(aes(yintercept = mean(pr_BL)), color = "red", linetype = "dashed")+
  facet_grid(~cutoff)+
  ylim(0,1)+
  ylab("PR-AUC (in-train)")+
  theme_bw()+
  theme(axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        legend.position = "bottom",
        legend.title = element_blank())

task_I_in_train_pr_plot

ggsave(plot = task_I_out_train_pr_plot,
       filename = paste0("figures/Task I PR-AUC.png"), 
       width = 9,
       height = 5)

ggsave(plot = task_I_in_train_pr_plot,
       filename = paste0("figures/Task I PR-AUC in-train.png"), 
       width = 9,
       height = 5)

# Out-train performance figure ROC-AUC
task_I_out_train_roc_plot <- exp_I_df %>%
  filter(per_type == "out-train") %>%
  mutate(cutoff = factor(cutoff, levels = c(1, 3, 7, 14, 21),
                         labels = c("<= 1 days", "<= 3 days",
                                    "<= 7 days","<= 14 days","<= 21 days"))) %>%
  ggplot(aes(feature_list, roc_auc, fill = feature_list))+
  geom_boxplot(linewidth = 0.1)+
  geom_hline(yintercept = 0.5, color = "red", linetype = "dashed")+
  facet_grid(~cutoff)+
  ylim(0.5,1)+
  ylab("ROC-AUC (out-train)")+
  theme_bw()+
  theme(axis.text.x = element_blank(), axis.title.x = element_blank(),
        legend.position = "bottom", legend.title = element_blank())

task_I_out_train_roc_plot

# In-train performance figure ROC-AUC
task_I_in_train_roc_plot <- exp_I_df %>%
  filter(per_type == "in-train") %>%
  mutate(cutoff = factor(cutoff, levels = c(1, 3, 7, 14, 21),
                         labels = c("<= 1 days", "<= 3 days",
                                    "<= 7 days","<= 14 days","<= 21 days"))) %>%
  ggplot(aes(feature_list, roc_auc, fill = feature_list))+
  geom_boxplot(linewidth = 0.1)+
  geom_hline(yintercept = 0.5, color = "red", linetype = "dashed")+
  facet_grid(~cutoff)+
  ylim(0.5,1)+
  ylab("ROC-AUC (in-train)")+
  theme_bw()+
  theme(axis.text.x = element_blank(), axis.title.x = element_blank(),
        legend.position = "bottom", legend.title = element_blank())

task_I_in_train_roc_plot

ggsave(plot = task_I_out_train_roc_plot,
       filename = paste0("figures/Task I ROC-AUC.png"), 
       width = 9,
       height = 5)

ggsave(plot = task_I_in_train_roc_plot,
       filename = paste0("figures/Task I ROC-AUC in-train.png"), 
       width = 9,
       height = 5)

Experiment II

Blood trajectory feature set

Not run during knitting

# Initialize list to store experimental results for each time cutoff
exp_II_list <- list()

# Iterate over different time windows (in days) for prediction inputs
for (cutoff in c(1,3, 7,14, 21)) {
 
 cat("Exp II raw, cutoff:", cutoff, "\n")
  
 # Load predicted probabilities for the current cutoff
 predict_list <- readRDS(paste0("prediction_experiments/predict_list_taskI_II_", cutoff, "days.Rds"))
 
 # Combine predictions, add subject-level metadata, filter and reformat
 predict_df <- do.call(rbind, predict_list) %>%
  left_join(subject_id_df) %>%
  left_join(mimic_patients_all) %>%
  filter(!analyte %in% c("Bicarbonate", "Calcium, Total", "Creatinine")) %>%
  select(prob1, prob2, prob3, analyte, subject_id, cohort_group) %>%
  filter(cohort_group != "SCI_noFracture") %>%
  mutate(is_SCI = ifelse(cohort_group == "SCI_Fracture", "yes", "no")) %>%
  pivot_wider(
   id_cols = c(subject_id, is_SCI),
   names_from = analyte,
   values_from = c(prob1, prob2, prob3)
  )
 
 # Drop columns with all missing values and remove incomplete rows
 predict_df <- predict_df %>%
  select(-all_of(names(which(sapply(predict_df, function(x) mean(is.na(x))) == 1))))
 
  # Mean impute remaining missing values
  predict_df[3:ncol(predict_df)] <- lapply(predict_df[3:ncol(predict_df)], function(x) {
    x[is.na(x)] <- mean(x, na.rm = TRUE)
    return(x)
  })
 
 # Count class labels for SCI classification
 n_yes <- table(predict_df$is_SCI)[2]
 n_no  <- table(predict_df$is_SCI)[1]
 
 # Run prediction experiment and store results
 exp_II_list[[as.character(cutoff)]] <- run_experiment(
  predict_df,
  "is_SCI", 
  n.yes.train = n_yes * 0.8, 
  n.no.train  = n_no * 0.8,
  resampling  = FALSE,
  reps        = nrep,
  method      = "glmnet"
 )
}

# Save the full list of experiments with a date tag
saveRDS(exp_II_list, paste0("prediction_experiments/exp_II_list_no_balance.RDS"))

Blood trajectory + summary stats feature set

Not run during knitting

# Initialize list to store experimental results for each time cutoff
exp_II_both_list <- list()

# Iterate over different time windows (in days) for prediction inputs
for (cutoff in c(1,3,7, 14, 21)){
 cat("Exp II raw, cutoff:", cutoff, "\n")
 
 predict_list <- readRDS(paste0("prediction_experiments/predict_list_taskI_II_", cutoff, "days.Rds"))
 
 # Extract performance metrics for each cutoff
 predict_traj_df <- do.call(rbind, predict_list) %>%
  left_join(subject_id_df) %>%
  left_join(mimic_patients_all) %>%
  filter(!analyte %in%c("Bicarbonate", "Calcium, Total", "Creatinine")) %>%
  select(prob1, prob2, prob3, analyte, subject_id, cohort_group) %>%
  filter(cohort_group != "SCI_noFracture") %>%
  mutate(is_SCI = ifelse(cohort_group == "SCI_Fracture", "yes", "no")) %>%
  pivot_wider(id_cols =c(subject_id, is_SCI),
              names_from = analyte, values_from = c(prob1, prob2, prob3))
 
 # Drop columns with all missing values and remove incomplete rows
  predict_traj_df <- predict_traj_df %>%
  select(-all_of(names(which(sapply(predict_traj_df , function(x) mean(is.na(x))) == 1))))
 
 
 # Combine predictions, add subject-level metadata, filter and reformat
 predict_raw_df <- mimic_modeling_set_f %>%
  filter(time_days<=cutoff) %>%
  select(subject_id,label, value_clean) %>%
  group_by(subject_id, label) %>%
  summarise(mean = mean(value_clean, na.rm = T),
            sd = sd(value_clean, na.rm = T),
            max = max(value_clean, na.rm = T),
            min = min(value_clean, na.rm = T)) %>%
  pivot_wider(id_cols =subject_id,
              names_from = label,
              values_from = c(mean, sd, max, min)) %>%
  left_join(mimic_patients_all%>%
             select(subject_id, cohort_group)) %>%
  filter(cohort_group != "SCI_noFracture") %>%
  mutate(is_SCI = ifelse(cohort_group == "SCI_Fracture", "yes", "no")) %>%
  relocate(is_SCI, .after = subject_id)%>%
  select(-cohort_group)
 
 
 predict_df <- predict_traj_df %>%
  full_join(predict_raw_df)
 
  # Mean impute remaining missing values
  predict_df[3:ncol(predict_df)] <- lapply(predict_df[3:ncol(predict_df)], function(x) {
    x[is.na(x)] <- mean(x, na.rm = TRUE)
    return(x)
  })
 
 n_yes <- table(predict_df$is_SCI)[2]
 n_no <- table(predict_df$is_SCI)[1]

 exp_II_both_list[[as.character(cutoff)]] <- run_experiment(
  predict_df, 
  "is_SCI", 
  n.yes.train = n_yes*0.8, 
  n.no.train = n_no*0.8,
  resampling = FALSE,
  reps = nrep,
  method = "glmnet")
}

saveRDS(exp_II_both_list, paste0("prediction_experiments/exp_II_both_list_no_balance.Rds"))

Blood trajectory + summary stats + BL feature set

Not run during knitting

# Initialize list to store results of augmented prediction experiments (trajectory + raw statistics)
exp_II_add_list <- list()

# Loop over different cutoff windows (in days)
for (cutoff in c(1,3, 7, 14, 21)) {
  
  cat("Exp II raw, cutoff:", cutoff, "\n")
  
  # Load predicted trajectory-based features for the given cutoff
  predict_list <- readRDS(paste0("prediction_experiments/predict_list_taskI_II_", cutoff, "days.Rds"))
  
  # Combine predictions and enrich with cohort information
  predict_traj_df <- do.call(rbind, predict_list) %>%
    left_join(subject_id_df) %>%
    left_join(mimic_patients_all) %>%
    filter(!analyte %in% c("Bicarbonate", "Calcium, Total", "Creatinine")) %>%
    select(prob1, prob2, prob3, analyte, subject_id, cohort_group) %>%
    filter(cohort_group != "SCI_noFracture") %>%
    mutate(is_SCI = ifelse(cohort_group == "SCI_Fracture", "yes", "no")) %>%
    pivot_wider(
      id_cols = c(subject_id, is_SCI),
      names_from = analyte,
      values_from = c(prob1, prob2, prob3)
    )
  
  # Drop columns with only missing values
  predict_traj_df <- predict_traj_df %>%
    select(-all_of(names(which(sapply(predict_traj_df, function(x) mean(is.na(x))) == 1))))
  
  # Create raw statistical summaries from cleaned lab values within the cutoff window
  predict_raw_df <- mimic_modeling_set_f %>%
    filter(time_days <= cutoff) %>%
    select(subject_id, label, value_clean) %>%
    group_by(subject_id, label) %>%
    summarise(
      mean = mean(value_clean, na.rm = TRUE),
      sd   = sd(value_clean, na.rm = TRUE),
      max  = max(value_clean, na.rm = TRUE),
      min  = min(value_clean, na.rm = TRUE),
      .groups = "drop"
    ) %>%
    pivot_wider(
      id_cols = subject_id,
      names_from = label,
      values_from = c(mean, sd, max, min)
    ) %>%
    left_join(mimic_patients_all %>% select(subject_id, cohort_group)) %>%
    filter(cohort_group != "SCI_noFracture") %>%
    mutate(is_SCI = ifelse(cohort_group == "SCI_Fracture", "yes", "no")) %>%
    relocate(is_SCI, .after = subject_id) %>%
    select(-cohort_group)
  
  # Merge trajectory-based and raw statistical features
  predict_df <- predict_traj_df %>%
    full_join(predict_raw_df)
  
  # Mean imputation for remaining missing values
  predict_df[3:ncol(predict_df)] <- sapply(
   predict_df[3:ncol(predict_df)], function(x) {
    x[is.na(x)] <- mean(x, na.rm = TRUE)
    return(x)
   })
  
  # Add demographic features
  predict_df <- predict_df %>%
    left_join(mimic_patients_all %>% select(subject_id, age, gender, insurance, ethnicity)) %>%
    filter(complete.cases(.))
  
  # Count class balance for SCI classification
  n_yes <- table(predict_df$is_SCI)[2]
  n_no  <- table(predict_df$is_SCI)[1]
  
  # Run prediction experiment using glmnet and save result
  exp_II_add_list[[as.character(cutoff)]] <- run_experiment(
    predict_df,
    "is_SCI",
    n.yes.train = n_yes * 0.8,
    n.no.train  = n_no * 0.8,
    resampling  = FALSE,
    reps        = nrep,
    method      = "glmnet"
  )
}

# Save the full list of augmented experiments
saveRDS(exp_II_add_list, paste0("prediction_experiments/exp_II_add_list_no_balance.RDS"))

Summary performance

## blood only
# Load results of prediction experiments
exp_II_list <- readRDS(paste0("prediction_experiments/exp_II_list_no_balance.RDS"))

# Drop any NULL entries (failed experiments)
exp_II_list <- exp_II_list[which(sapply(exp_II_list, function(x) !is.null(x)))]
exp_II_per_df <- experiment_performance(exp_II_list, nrep)

## blood + summary stats
# Load results of prediction experiments
exp_II_both_list <- readRDS(paste0("prediction_experiments/exp_II_both_list_no_balance.RDS"))

# Drop any NULL entries (failed experiments)
exp_II_both_list <- exp_II_both_list[which(sapply(exp_II_both_list, function(x) !is.null(x)))]

# Extract and reshape performance metrics across all cutoff experiments
exp_II_both_per_df <- experiment_performance(exp_II_both_list, nrep)

## blood + summary stats +bl
# Load results of prediction experiments
exp_II_add_list <- readRDS(paste0("prediction_experiments/exp_II_add_list_no_balance.RDS"))

# Drop any NULL entries (failed experiments)
exp_II_add_list <- exp_II_add_list[which(sapply(exp_II_add_list, function(x) !is.null(x)))]
exp_II_add_per_df <- experiment_performance(exp_II_add_list, nrep)

exp_II_per_df$feature_list <- "Traj. PPA"
exp_II_both_per_df$feature_list <- "Traj. PPA + Sum. stats"
exp_II_add_per_df$feature_list <- "Traj. PPA + Sum. stats + BL"

exp_II_df <- bind_rows(exp_II_per_df, exp_II_both_per_df, exp_II_add_per_df)

saveRDS(exp_II_df, paste0("prediction_experiments/exp_II_df.RDS"))

Hypothesis testing

## Across feature list
f_result_featureList_II <- list()

for (c in c(1, 3, 7, 14, 21)){
  x <- exp_II_df %>%
    filter(cutoff == c) %>%
    group_by(feature_list) %>%
    mutate(block = 1:n())
  
  f_result <- friedman.test(x$pr_auc, x$feature_list, x$block)
  
  f_result_featureList_II[[c]] <- data.frame(
    cutoff = c,
    chi_squared = f_result$statistic,
    p_value = sprintf("%.7f", f_result$p.value)
  )
}

f_result_featureList_II_df <- bind_rows(f_result_featureList_II)

## Across time for each feature list

f_result_cutoff_II <- list()

for (f in unique(exp_II_df$feature_list)){
  x <- exp_II_df %>%
    filter(feature_list == f) %>%
    group_by(cutoff) %>%
    mutate(block = 1:n())
  
  f_result <- friedman.test(x$pr_auc, x$cutoff, x$block)
  
  f_result_cutoff_II[[f]] <- data.frame(
    feature_list = f,
    chi_squared = f_result$statistic,
    p_value = sprintf("%.7f", f_result$p.value)
  )
}

f_result_cutoff_II_df <- bind_rows(f_result_cutoff_II)

kable(f_result_featureList_II_df, caption = "Friedman Test Results by Cutoff") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

Friedman Test Results by Cutoff
	cutoff	chi_squared
Friedman chi-squared…1	1	48.52
Friedman chi-squared…2	3	72.84
Friedman chi-squared…3	7	52.96
Friedman chi-squared…4	14	62.44
Friedman chi-squared…5	21	45.72

kable(f_result_cutoff_II_df, caption = "Friedman Test Results by Feature List") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

Friedman Test Results by Feature List
	feature_list	chi_squared
Friedman chi-squared…1	Traj. PPA	134.672
Friedman chi-squared…2	Traj. PPA + Sum. stats	123.792
Friedman chi-squared…3	Traj. PPA + Sum. stats + BL	108.272

Figure Exp II

task_II_out_train_pr_plot <- exp_II_df %>%
  filter(per_type == "out-train") %>%
  mutate(cutoff = factor(cutoff, levels = c(1, 3, 7, 14, 21),
                         labels = c("<= 1 days", "<= 3 days",
                                    "<= 7 days","<= 14 days","<= 21 days"))) %>%
  ggplot(aes(feature_list, pr_auc, fill = feature_list))+
  geom_boxplot(linewidth = 0.1)+
  geom_hline(aes(yintercept = mean(pr_BL)), color = "red", linetype = "dashed")+
  facet_grid(~cutoff)+
  ylim(0,1)+
  theme_bw()+
  ylab("PR-AUC (out-train)")+
  theme(axis.text.x = element_blank(), 
        axis.title.x = element_blank(),
        legend.position = "bottom", 
        legend.title = element_blank())

task_II_out_train_pr_plot

task_II_in_train_pr_plot <- exp_II_df %>%
  filter(per_type == "in-train") %>%
  mutate(cutoff = factor(cutoff, levels = c(1, 3, 7, 14, 21),
                         labels = c("<= 1 days", "<= 3 days",
                                    "<= 7 days","<= 14 days","<= 21 days"))) %>%
  ggplot(aes(feature_list, pr_auc, fill = feature_list))+
  geom_boxplot(linewidth = 0.1)+
  geom_hline(aes(yintercept = mean(pr_BL)), color = "red", linetype = "dashed")+
  facet_grid(~cutoff)+
  ylim(0,1)+
  ylab("PR-AUC (in-train)")+
  theme_bw()+
  theme(axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        legend.position = "bottom",
        legend.title = element_blank())

task_II_in_train_pr_plot

ggsave(plot = task_II_out_train_pr_plot,
       filename = paste0("figures/Task II PR-AUC out_train.png"), 
       width = 9,
       height = 5)

ggsave(plot = task_II_in_train_pr_plot,
       filename = paste0("figures/Task II PR-AUC in_train.png"), 
       width = 9,
       height = 5)

task_II_out_train_roc_plot <- exp_II_df %>%
  filter(per_type == "out-train") %>%
  mutate(cutoff = factor(cutoff, levels = c(1, 3, 7, 14, 21),
                         labels = c("<= 1 days", "<= 3 days",
                                    "<= 7 days","<= 14 days","<= 21 days"))) %>%
  ggplot(aes(feature_list, roc_auc, fill = feature_list))+
  geom_boxplot(linewidth = 0.1)+
  geom_hline(yintercept = 0.5, color = "red", linetype = "dashed")+
  facet_grid(~ cutoff)+
  ylim(0.5,1)+
  theme_bw()+
  ylab("ROC-AUC (out-train)")+
  theme(axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        legend.position = "bottom",
        legend.title = element_blank())

task_II_out_train_roc_plot

task_II_in_train_roc_plot <- exp_II_df %>%
  filter(per_type == "in-train") %>%
  mutate(cutoff = factor(cutoff, levels = c(1, 3, 7, 14, 21),
                         labels = c("<= 1 days", "<= 3 days",
                                    "<= 7 days","<= 14 days","<= 21 days"))) %>%
  ggplot(aes(feature_list, roc_auc, fill = feature_list))+
  geom_boxplot(linewidth = 0.1)+
  geom_hline(yintercept = 0.5, color = "red", linetype = "dashed")+
  facet_grid(~cutoff)+
  ylim(0.5,1)+
  ylab("ROC-AUC (in-train)")+
  theme_bw()+
  theme(axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        legend.position = "bottom",
        legend.title = element_blank())

task_II_in_train_roc_plot

ggsave(plot = task_II_out_train_roc_plot,
       filename = paste0("figures/Task II ROC-AUC out_train.png"), 
       width = 9,
       height = 5)

ggsave(plot = task_II_in_train_roc_plot,
       filename = paste0("figures/Task II ROC-AUC in_train.png"), 
       width = 9,
       height = 5)

Experiment III

Experiment III makes use of a dataset obtained from the TRACK-SCI study. Data is not provided and therefore code will not run.

Blood trajectory feature set

Not run during knitting

exp_III_list<-list()
for (cutoff in c(1,3,7,14,21)){
  
  # cutoff<-21
  cat("Exp III, cutoff:", cutoff, "\n")
  predict_list<-readRDS(paste0("prediction_experiments/predict_list_taskIII_",cutoff,
                        "days.Rds"))
  
  predict_df<-do.call(rbind,predict_list)%>%
    left_join(track_static_df)%>%
    filter(!analyte %in%c("Bicarbonate", "Calcium, Total", "Creatinine"))%>%
    select(prob1, prob2, prob3, analyte, subject_id_num, latest_AIS_at_hospital)%>%
    mutate(AIS_binary = ifelse(latest_AIS_at_hospital %in% c("A", "B"), "AB","CDE"))%>%
    pivot_wider(id_cols =c(subject_id_num, AIS_binary),
              names_from = analyte, values_from = c(prob1, prob2, prob3))
  
  # Drop columns with all missing values
  predict_df <- predict_df %>%
    select(-all_of(names(which(sapply(predict_df, function(x) mean(is.na(x))) == 1))))
  
  # Mean impute remaining missing values
  predict_df[3:ncol(predict_df)] <- lapply(predict_df[3:ncol(predict_df)], function(x) {
    x[is.na(x)] <- mean(x, na.rm = TRUE)
    return(x)
  })
  
  n_yes<-table(predict_df$AIS_binary)[1]
  n_no<-table(predict_df$AIS_binary)[2]
  
  exp_III_list[[as.character(cutoff)]]<-run_experiment(predict_df, "AIS_binary", 
                                       n.yes.train = n_yes*0.8, reps = nrep,
                                       n.no.train = n_no*0.8,
                                       yes_name = "AB",
                                       no_name = "CDE",resampling = F,
                                       method = "glmnet")
}

saveRDS(exp_III_list, "prediction_experiments/exp_III_list_no_balance.RDS")

Blood trajectory + summary stats feature set

Not run during knitting

exp_III_both_list<-list()

for (cutoff in c(1,3,7,14,21)){
  
  # cutoff<-1
  
  cat("Exp III raw, cutoff:", cutoff, "\n")
  
  predict_list<-readRDS(paste0("prediction_experiments/predict_list_taskIII_",cutoff,
                        "days.Rds"))

  predict_traj_df<-do.call(rbind,predict_list)%>%
    left_join(track_static_df)%>%
    filter(!analyte %in%c("Bicarbonate", "Calcium, Total", "Creatinine"))%>%
    select(prob1, prob2, prob3, analyte,subject_id_num , latest_AIS_at_hospital)%>%
    mutate(AIS_binary = ifelse(latest_AIS_at_hospital %in% c("A", "B"), "AB","CDE"))%>%
    pivot_wider(id_cols =c(subject_id_num, AIS_binary),
              names_from = analyte, values_from = c(prob1, prob2, prob3))
  
    predict_traj_df<-predict_traj_df%>%
    select(-all_of(names(which(sapply(predict_traj_df, 
                                      function(x) mean(is.na(x)))==1))))

    predict_raw_df<-track_sci_odc%>%
      filter(time_days<=cutoff)%>%
      select(subject_id_num,lab_name, value_clean)%>%
      group_by(subject_id_num, lab_name)%>%
      summarise(mean = mean(value_clean, na.rm = T),
                sd = sd(value_clean, na.rm = T),
                max = max(value_clean, na.rm = T),
                min = min(value_clean, na.rm = T))%>%
      pivot_wider(id_cols =subject_id_num, names_from = lab_name,
                  values_from = c(mean, sd, max, min))
  
  
    predict_df<-predict_traj_df%>%
      full_join(predict_raw_df)
    
    # Mean impute remaining missing values
    predict_df[3:ncol(predict_df)] <- lapply(predict_df[3:ncol(predict_df)], function(x) {
      x[is.na(x)] <- mean(x, na.rm = TRUE)
      return(x)
    })

    predict_df<-predict_df%>%
      filter(complete.cases(.))
    
  n_yes<-table(predict_df$AIS_binary)[1]
  n_no<-table(predict_df$AIS_binary)[2]
  
  exp_III_both_list[[as.character(cutoff)]]<-run_experiment(predict_df, "AIS_binary", 
                                       n.yes.train = n_yes*0.8, reps = nrep,
                                       n.no.train = n_no*0.8,
                                       yes_name = "AB",
                                       no_name = "CDE",resampling = F,
                                       method = "glmnet")
}

saveRDS(exp_III_both_list, "prediction_experiments/exp_III_both_list_no_balance.RDS")
print("Done")

Blood trajectory + summary stats + BL feature set

Not run during knitting

exp_III_add_list<-list()

for (cutoff in c(1,3,7,14,21)){
  
  cat("Exp III raw, cutoff:", cutoff, "\n")
  
  predict_list<-readRDS(paste0("prediction_experiments/predict_list_taskIII_",cutoff,
                        "days.Rds"))

  predict_traj_df<-do.call(rbind,predict_list)%>%
    left_join(track_static_df)%>%
    filter(!analyte %in%c("Bicarbonate", "Calcium, Total", "Creatinine"))%>%
    select(prob1, prob2, prob3, analyte,subject_id_num , latest_AIS_at_hospital)%>%
    mutate(AIS_binary = ifelse(latest_AIS_at_hospital %in% c("A", "B"), "AB","CDE"))%>%
    pivot_wider(id_cols =c(subject_id_num, AIS_binary),
              names_from = analyte, values_from = c(prob1, prob2, prob3))
  
    predict_traj_df<-predict_traj_df%>%
    select(-all_of(names(which(sapply(predict_traj_df, 
                                      function(x) mean(is.na(x)))==1))))

    predict_raw_df<-track_sci_odc%>%
      filter(time_days<=cutoff)%>%
      select(subject_id_num,lab_name, value_clean)%>%
      group_by(subject_id_num, lab_name)%>%
      summarise(mean = mean(value_clean, na.rm = T),
                sd = sd(value_clean, na.rm = T),
                max = max(value_clean, na.rm = T),
                min = min(value_clean, na.rm = T))%>%
      pivot_wider(id_cols =subject_id_num, names_from = lab_name,
                  values_from = c(mean, sd, max, min))
  
  
    predict_df<-predict_traj_df%>%
      full_join(predict_raw_df)
    
    # Mean impute remaining missing values
    predict_df[3:ncol(predict_df)] <- lapply(predict_df[3:ncol(predict_df)], function(x) {
      x[is.na(x)] <- mean(x, na.rm = TRUE)
      return(x)
    })
    
    predict_df<-predict_df%>%
    left_join(track_static_df%>%select(subject_id_num, age, gender))%>%
    filter(complete.cases(.))
    
    n_yes<-table(predict_df$AIS_binary)[1]
    n_no<-table(predict_df$AIS_binary)[2]
    
    exp_III_add_list[[as.character(cutoff)]]<-run_experiment(predict_df, "AIS_binary", 
                                       n.yes.train = n_yes*0.8, reps = nrep,
                                       n.no.train = n_no*0.8,
                                       yes_name = "AB",
                                       no_name = "CDE",resampling = F,
                                       method = "glmnet")
}

saveRDS(exp_III_add_list, "prediction_experiments/exp_III_add_list_no_balance.RDS")

Summary performance

## blood only
# Load results of prediction experiments
exp_III_list <- readRDS(paste0("prediction_experiments/exp_III_list_no_balance.RDS"))

# Drop any NULL entries (failed experiments)
exp_III_list <- exp_III_list[which(sapply(exp_III_list, function(x) !is.null(x)))]
exp_III_per_df <- experiment_performance(exp_III_list, nrep)

## blood + summary stats
# Load results of prediction experiments
exp_III_both_list <- readRDS(paste0("prediction_experiments/exp_III_both_list_no_balance.RDS"))

# Drop any NULL entries (failed experiments)
exp_III_both_list <- exp_III_both_list[which(sapply(exp_III_both_list, function(x) !is.null(x)))]

# Extract and reshape performance metrics across all cutoff experiments
exp_III_both_per_df <- experiment_performance(exp_III_both_list, nrep)

## blood + summary stats +bl
# Load results of prediction experiments
exp_III_add_list <- readRDS(paste0("prediction_experiments/exp_III_add_list_no_balance.RDS"))

# Drop any NULL entries (failed experiments)
exp_III_add_list <- exp_III_add_list[which(sapply(exp_III_add_list, function(x) !is.null(x)))]
exp_III_add_per_df <- experiment_performance(exp_III_add_list, nrep)

exp_III_per_df$feature_list <- "Traj. PPA"
exp_III_both_per_df$feature_list <- "Traj. PPA + Sum. stats"
exp_III_add_per_df$feature_list <- "Traj. PPA + Sum. stats + BL"

exp_III_df <- bind_rows(exp_III_per_df, exp_III_both_per_df, exp_III_add_per_df)

saveRDS(exp_III_df, "prediction_experiments/exp_III_df.RDS")

Hypothesis testing

## Across feature list
f_result_featureList_III <- list()

for (c in c(1, 3, 7, 14, 21)){
  x <- exp_III_df %>%
    filter(cutoff == c) %>%
    group_by(feature_list) %>%
    mutate(block = 1:n())
  
  f_result <- friedman.test(x$pr_auc, x$feature_list, x$block)
  
  f_result_featureList_III[[c]] <- data.frame(
    cutoff = c,
    chi_squared = f_result$statistic,
    p_value = sprintf("%.7f", f_result$p.value)
  )
}

f_result_featureList_III_df <- bind_rows(f_result_featureList_III)

## Across time for each feature list

f_result_cutoff_III <- list()

for (f in unique(exp_III_df$feature_list)){
  x <- exp_III_df %>%
    filter(feature_list == f) %>%
    group_by(cutoff) %>%
    mutate(block = 1:n())
  
  f_result <- friedman.test(x$pr_auc, x$cutoff, x$block)
  
  f_result_cutoff_III[[f]] <- data.frame(
    feature_list = f,
    chi_squared = f_result$statistic,
    p_value = sprintf("%.7f", f_result$p.value)
  )
}

f_result_cutoff_III_df <- bind_rows(f_result_cutoff_III)

kable(f_result_featureList_III_df, caption = "Friedman Test Results by Cutoff") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

Friedman Test Results by Cutoff
	cutoff	chi_squared	p_value
Friedman chi-squared…1	1	36.133333	0.0000000
Friedman chi-squared…2	3	65.010101	0.0000000
Friedman chi-squared…3	7	64.161616	0.0000000
Friedman chi-squared…4	14	19.555556	0.0000567
Friedman chi-squared…5	21	3.428571	0.1800923

kable(f_result_cutoff_III_df, caption = "Friedman Test Results by Feature List") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

Friedman Test Results by Feature List
	feature_list	chi_squared	p_value
Friedman chi-squared…1	Traj. PPA	97.36000	0.0000000
Friedman chi-squared…2	Traj. PPA + Sum. stats	13.17671	0.0104439
Friedman chi-squared…3	Traj. PPA + Sum. stats + BL	17.17435	0.0017878

Figure Exp III

task_III_out_train_pr_plot <- as.data.frame(exp_III_df) %>%
  filter(per_type == "out-train") %>%
  mutate(cutoff = factor(cutoff, levels = c(1, 3, 7, 14, 21),
                         labels = c("<= 1 days", "<= 3 days",
                                    "<= 7 days","<= 14 days","<= 21 days"))) %>%
  ggplot(aes(feature_list, pr_auc, fill = feature_list))+
  geom_boxplot(linewidth = 0.1)+
  geom_hline(aes(yintercept = mean(pr_BL)), color = "red", linetype = "dashed")+
  facet_grid(~cutoff)+
  ylim(0,1)+
  theme_bw()+
  ylab("PR-AUC (out-train)")+
  theme(axis.text.x = element_blank(), 
        axis.title.x = element_blank(),
        legend.position = "bottom", 
        legend.title = element_blank())

task_III_out_train_pr_plot

task_III_in_train_pr_plot <- exp_III_df %>%
  filter(per_type == "in-train") %>%
  mutate(cutoff = factor(cutoff, levels = c(1, 3, 7, 14, 21),
                         labels = c("<= 1 days", "<= 3 days",
                                    "<= 7 days","<= 14 days","<= 21 days"))) %>%
  ggplot(aes(feature_list, pr_auc, fill = feature_list))+
  geom_boxplot(linewidth = 0.1)+
  geom_hline(aes(yintercept = mean(pr_BL)), color = "red", linetype = "dashed")+
  facet_grid(~cutoff)+
  ylim(0,1)+
  ylab("PR-AUC (in-train)")+
  theme_bw()+
  theme(axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        legend.position = "bottom",
        legend.title = element_blank())

task_III_in_train_pr_plot

ggsave(plot = task_III_out_train_pr_plot,
       filename = paste0("figures/Task III PR-AUC out_train.png"), 
       width = 9,
       height = 5)

ggsave(plot = task_III_in_train_pr_plot,
       filename = paste0("figures/Task III PR-AUC in_train.png"), 
       width = 9,
       height = 5)

task_III_out_train_roc_plot <- exp_III_df %>%
  filter(per_type == "out-train") %>%
  mutate(cutoff = factor(cutoff, levels = c(1, 3, 7, 14, 21),
                         labels = c("<= 1 days", "<= 3 days",
                                    "<= 7 days","<= 14 days","<= 21 days"))) %>%
  ggplot(aes(feature_list, roc_auc, fill = feature_list))+
  geom_boxplot(linewidth = 0.1)+
  geom_hline(yintercept = 0.5, color = "red", linetype = "dashed")+
  facet_grid(~ cutoff)+
  ylim(0.5,1)+
  theme_bw()+
  ylab("ROC-AUC (out-train)")+
  theme(axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        legend.position = "bottom",
        legend.title = element_blank())

task_III_out_train_roc_plot

task_III_in_train_roc_plot <- exp_III_df %>%
  filter(per_type == "in-train") %>%
  mutate(cutoff = factor(cutoff, levels = c(1, 3, 7, 14, 21),
                         labels = c("<= 1 days", "<= 3 days",
                                    "<= 7 days","<= 14 days","<= 21 days"))) %>%
  ggplot(aes(feature_list, roc_auc, fill = feature_list))+
  geom_boxplot(linewidth = 0.1)+
  geom_hline(yintercept = 0.5, color = "red", linetype = "dashed")+
  facet_grid(~cutoff)+
  ylim(0.5,1)+
  ylab("ROC-AUC (in-train)")+
  theme_bw()+
  theme(axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        legend.position = "bottom",
        legend.title = element_blank())

task_III_in_train_roc_plot

ggsave(plot = task_III_out_train_roc_plot,
       filename = paste0("figures/Task III ROC-AUC out_train.png"), 
       width = 9,
       height = 5)

ggsave(plot = task_III_in_train_roc_plot,
       filename = paste0("figures/Task III ROC-AUC in_train.png"), 
       width = 9,
       height = 5)

Tables and Figures

Tables

exp_I_df$Experiment<-"Exp. I"
exp_II_df$Experiment<-"Exp. II"
exp_III_df$Experiment<-"Exp. III"

exp_all_df<-bind_rows(exp_I_df, exp_II_df, exp_III_df)

exp_all_summary_df<-exp_all_df%>%
  group_by(Experiment, per_type, cutoff, feature_list)%>%
  summarise(mean_roc_auc = mean(roc_auc),
            se_roc_auc = sd(roc_auc)/sqrt(n()),
            low_ci_roc_auc = mean_roc_auc - 1.96*se_roc_auc,
            high_ci_roc_auc = mean_roc_auc + 1.96*se_roc_auc,
            mean_pr_auc = mean(pr_auc),
            se_pr_auc = sd(pr_auc)/sqrt(n()),
            low_ci_pr_auc = mean_pr_auc - 1.96*se_pr_auc,
            high_ci_pr_auc = mean_pr_auc + 1.96*se_pr_auc,
            mean_pr_BL = mean(pr_BL),
            # text summary for printing
            `ROC AUC` = paste0(
              sprintf("%.2f", mean_roc_auc),
              " [", sprintf("%.2f", low_ci_roc_auc), "-", 
              sprintf("%.2f", high_ci_roc_auc), "]"),
            `PR AUC` = paste0(
              sprintf("%.2f", mean_pr_auc),
              " [", sprintf("%.2f", low_ci_pr_auc), "-", 
              sprintf("%.2f", high_ci_pr_auc), "]"),
            Prevalence = sprintf("%.2f", mean_pr_BL))%>%
  pivot_wider(id_cols = c(per_type, Experiment, feature_list), names_from = c(cutoff), values_from = c(`ROC AUC`, `PR AUC`, Prevalence))

##roc_table_all
exp_all_summary_df%>%
  ungroup()%>%
  filter(per_type =="out-train")%>%
  select(Experiment, feature_list, paste0("ROC AUC_", c("1", "3", "7", "14", "21")))%>%
  kableExtra::kbl(row.names = F)%>%
  kable_styling(full_width = F)%>%
  kable_classic()%>%
  kableExtra::save_kable(file = "tables/exp_all_ROC_out_train.html")

##roc_table_all
exp_all_summary_df%>%
  ungroup()%>%
  filter(per_type =="in-train")%>%
  select(Experiment, feature_list, paste0("ROC AUC_", c("1", "3", "7", "14", "21")))%>%
  kableExtra::kbl(row.names = F)%>%
  kable_styling(full_width = F)%>%
  kable_classic()%>%
  kableExtra::save_kable(file = "tables/exp_all_ROC_in_train.html")

##PR_table_all
exp_all_summary_df%>%
  ungroup()%>%
  filter(per_type =="out-train")%>%
  select(Experiment, feature_list, paste0(c("PR AUC_", "Prevalence_"), 
                                          rep(c("1", "3", "7", "14", "21"), each = 2)))%>%
  kableExtra::kbl(row.names = F)%>%
  kable_styling(full_width = F)%>%
  kable_classic()%>%
  kableExtra::save_kable(file = "tables/exp_all_PR_out_train.html")

##PR_table_all
exp_all_summary_df%>%
  ungroup()%>%
  filter(per_type =="in-train")%>%

  select(Experiment, feature_list, paste0(c("PR AUC_", "Prevalence_"), 
                                          rep(c("1", "3", "7", "14", "21"), each = 2)))%>%
  kableExtra::kbl(row.names = F)%>%
  kable_styling(full_width = F)%>%
  kable_classic()%>%
  kableExtra::save_kable(file = "tables/exp_all_PR_in_train.html")

Figures

roc_all_out_train_fig<-
  task_I_out_train_roc_plot+
  task_II_out_train_roc_plot+
  task_III_out_train_roc_plot+plot_layout(ncol = 1)+
  plot_annotation(tag_levels = "a")

roc_all_out_train_fig

ggsave(plot = roc_all_out_train_fig, "figures/ROC_all_out_train_fig.png", height = 8, width = 6)

roc_all_in_train_fig<-
  task_I_in_train_roc_plot+
  task_II_in_train_roc_plot+
  task_III_in_train_roc_plot+plot_layout(ncol = 1)+
  plot_annotation(tag_levels = "a")

roc_all_in_train_fig

ggsave(plot = roc_all_in_train_fig, "figures/ROC_all_in_train_fig.png", height = 8, width = 6)

pr_all_out_train_fig<-
  task_I_out_train_pr_plot+
  task_II_out_train_pr_plot+
  task_III_out_train_pr_plot+plot_layout(ncol = 1)+
  plot_annotation(tag_levels = "a")

pr_all_out_train_fig

ggsave(plot = pr_all_out_train_fig, "figures/PR_all_out_train_fig.png", height = 8, width = 6)

pr_all_in_train_fig<-
  task_I_in_train_pr_plot+
  task_II_in_train_pr_plot+
  task_III_in_train_pr_plot+plot_layout(ncol = 1)+
  plot_annotation(tag_levels = "a")

pr_all_in_train_fig

ggsave(plot = pr_all_in_train_fig, "figures/PR_all_in_train_fig.png", height = 8, width = 6)

Experiments with SAPSII

SAPSII was obtained from MIMIC III and MIMIC IV using For MIMIC-III, we compute scores for the selected cohort using SQL code publicly available on GitHub. For MIMIC-IV, we used the equivalent script.

Since we could not compute SAPSII for all initial subjects in our MIMIC cohort, we re-run the ML experiments with the SAPSII subset only to allow for comparability between feature lists.

# Here we load the SAPSII score and create a new mimic_patient_all dataset with the subset of individuals with SAPSII

mimic_saps<-read.csv("mimic_SC_saps.csv") # This dataset is not provided and needs to be generated as indicated above

mimic_patients_all_saps<-mimic_patients_all%>%
  right_join(mimic_saps%>%mutate(subject_id = as.character(subject_id)))

SAPSII Experiment I

SAPSII alone

exp_I_saps_list <- list()

for (cutoff in c(1, 3, 7, 14, 21)){
  cat("Exp I, cutoff:", cutoff, "\n")
  
  predict_list <- readRDS(
    paste0("prediction_experiments/predict_list_taskI_II_",
           cutoff,
           "days.Rds")
  )
  
  predict_df<-  subject_id_df %>%
    left_join(mimic_patients_all_saps) %>%
    select(subject_id, hospital_expire_flag, sapsii) %>%
    mutate(is_dead = ifelse(hospital_expire_flag == 1, "yes", "no")) %>%
    select(subject_id, is_dead, sapsii) %>%
    filter(complete.cases(.)) 

  # Mean impute remaining missing values
  predict_df[3:ncol(predict_df)] <- lapply(predict_df[3:ncol(predict_df)], function(x) {
    x[is.na(x)] <- mean(x, na.rm = TRUE)
    return(x)
  })
  
  n_yes <- table(predict_df$is_dead)[2]
  n_no <- table(predict_df$is_dead)[1]
  
  exp_I_saps_list[[as.character(cutoff)]] <- run_experiment(
    predict_df, 
    "is_dead", 
    n.yes.train = n_yes*0.8, 
    n.no.train = n_no*0.8,
    resampling = F,
    method = "lr",
    reps = nrep)
}

saveRDS(exp_I_saps_list, "prediction_experiments/exp_I_saps_list_saps.RDS")

Blood trajectory feature set

# Run mortality prediction experiments for different time cutoffs
# This may take time depending on model complexity and number of repetitions
exp_I_saps_list_trj <- list()

for (cutoff in c(1, 3, 7, 14, 21)) {
  
  cat("Exp I, cutoff:", cutoff, "\n")
  
  # Load predictions for a given cutoff
  predict_list <- readRDS(paste0("prediction_experiments/predict_list_taskI_II_", cutoff, "days.Rds"))
  
  # Combine and enrich prediction data
  predict_df <- do.call(rbind, predict_list) %>%
    left_join(subject_id_df) %>%
    left_join(mimic_patients_all_saps) %>%
    filter(!analyte %in% c("Bicarbonate", "Calcium, Total", "Creatinine")) %>%
    select(prob1, prob2, prob3, analyte, subject_id, hospital_expire_flag) %>%
    mutate(is_dead = ifelse(hospital_expire_flag, "yes", "no")) %>%
    pivot_wider(
      id_cols = c(subject_id, is_dead),
      names_from = analyte,
      values_from = c(prob1, prob2, prob3)
    )
  
  # Drop columns with all missing values
  predict_df <- predict_df %>%
    select(-all_of(names(which(sapply(predict_df, function(x) mean(is.na(x))) == 1))))
  
  # Mean impute remaining missing values
  predict_df[3:ncol(predict_df)] <- lapply(predict_df[3:ncol(predict_df)], function(x) {
    x[is.na(x)] <- mean(x, na.rm = TRUE)
    return(x)
  })
  
  # Determine class counts
  n_yes <- table(predict_df$is_dead)[["yes"]]
  n_no  <- table(predict_df$is_dead)[["no"]]
  
  # Run predictive model experiment
  exp_I_saps_list_trj[[as.character(cutoff)]] <- run_experiment(
    predict_df,
    target.name = "is_dead",
    n.yes.train = n_yes * 0.8,
    n.no.train  = n_no * 0.8,
    resampling = F,
    reps = nrep,
    method = "glmnet"
  )
}

# Save the result of all experiments
saveRDS(exp_I_saps_list_trj, paste0("prediction_experiments/exp_I_saps_list_trj.Rds"))

Blood trajectory + summary stats feature set

Not run during knitting

exp_I_saps_list_both <- list()

for (cutoff in c(1,3, 7, 14 ,21)){
  cat("Exp I raw, cutoff:", cutoff, "\n")
  
  predict_list <- readRDS(paste0("prediction_experiments/predict_list_taskI_II_",
                                 cutoff,
                                 "days.Rds"))
  
  predict_traj_df <- do.call(rbind,predict_list) %>%
    left_join(subject_id_df) %>%
    left_join(mimic_patients_all_saps) %>%
    filter(!analyte %in% c("Bicarbonate", "Calcium, Total", "Creatinine")) %>%
    select(prob1, prob2, prob3, analyte, subject_id, hospital_expire_flag) %>%
    mutate(is_dead = ifelse(hospital_expire_flag, "yes", "no")) %>%
    pivot_wider(id_cols = c(subject_id, is_dead),
                names_from = analyte, values_from = c(prob1, prob2, prob3))
  
  predict_traj_df <- predict_traj_df %>%
    select(-all_of(names(which(sapply(predict_traj_df, 
                                      function(x) mean(is.na(x)))==1))))
  
  predict_raw_df <- mimic_modeling_set_f %>%
    filter(time_days <= cutoff) %>%
    select(subject_id,label, value_clean) %>%
    group_by(subject_id, label) %>%
    summarise(mean = mean(value_clean, na.rm = T),
              sd = sd(value_clean, na.rm = T),
              max = max(value_clean, na.rm = T),
              min = min(value_clean, na.rm = T)) %>%
    pivot_wider(id_cols = subject_id, names_from = label,
                values_from = c(mean, sd, max, min)) %>%
    right_join(mimic_patients_all_saps %>%
                select(subject_id, hospital_expire_flag)) %>%
    mutate(is_dead = ifelse(hospital_expire_flag == 1, "yes", "no")) %>%
    relocate(is_dead, .after = subject_id) %>%
    select(-hospital_expire_flag)
  
  
  predict_df <- predict_traj_df %>%
    right_join(predict_raw_df)
  
  # Mean impute remaining missing values
  predict_df[3:ncol(predict_df)] <- lapply(predict_df[3:ncol(predict_df)], function(x) {
    x[is.na(x)] <- mean(x, na.rm = TRUE)
    return(x)
  })
  
 n_yes <- table(predict_df$is_dead)[["yes"]]
 n_no <- table(predict_df$is_dead)[["no"]]
  
 exp_I_saps_list_both[[as.character(cutoff)]] <- run_experiment(
  predict_df, "is_dead", 
  n.yes.train = n_yes*0.8, 
  n.no.train = n_no*0.8,
  resampling = F,
  reps = nrep,
  method = "glmnet")
}

saveRDS(exp_I_saps_list_both , paste0("prediction_experiments/exp_I_saps_list_both.RDS"))

Blood trajectory + summary stats + BL feature set

Not run during knitting

exp_I_saps_list_add <- list()

for (cutoff in c(1, 3, 7, 14, 21)){
  
  cat("Exp I, cutoff:", cutoff, "\n")
  predict_list <- readRDS(paste0("prediction_experiments/predict_list_taskI_II_", cutoff,
                               "days.Rds"))
  
  predict_traj_df <- do.call(rbind,predict_list) %>%
    left_join(subject_id_df) %>%
    right_join(mimic_patients_all_saps) %>%
    filter(!analyte %in% c("Bicarbonate", "Calcium, Total", "Creatinine")) %>%
    select(prob1, prob2, prob3, analyte, subject_id, hospital_expire_flag) %>%
    mutate(is_dead = ifelse(hospital_expire_flag, "yes", "no"))%>%
    pivot_wider(id_cols =c(subject_id, is_dead),
                names_from = analyte,
                values_from = c(prob1, prob2, prob3))
  
  predict_traj_df <- predict_traj_df %>%
    select(-all_of(names(which(sapply(predict_traj_df, 
                                      function(x) mean(is.na(x)))==1))))
  
  predict_raw_df <- mimic_modeling_set_f %>%
    filter(subject_id%in%mimic_patients_all_saps$subject_id)%>%
    filter(time_days <= cutoff) %>%
    select(subject_id,label, value_clean) %>%
    group_by(subject_id, label) %>%
    summarise(mean = mean(value_clean, na.rm = T),
              sd = sd(value_clean, na.rm = T),
              max = max(value_clean, na.rm = T),
              min = min(value_clean, na.rm = T)) %>%
    pivot_wider(id_cols =subject_id,
                names_from = label,
                values_from = c(mean, sd, max, min))
  
  predict_df <- predict_traj_df %>%
    left_join(predict_raw_df)
  
  # Mean impute remaining missing values
  predict_df[3:ncol(predict_df)] <- lapply(predict_df[3:ncol(predict_df)], function(x) {
    x[is.na(x)] <- mean(x, na.rm = TRUE)
    return(x)
  })
  
  predict_df <- predict_df %>%
    right_join(mimic_patients_all %>% 
                select(subject_id, cohort_group, age, gender, insurance, ethnicity))%>%
    filter(complete.cases(.))
  
  n_yes <- table(predict_df$is_dead)[2]
  n_no <- table(predict_df$is_dead)[1]
  
  exp_I_saps_list_add[[as.character(cutoff)]] <- run_experiment(
   predict_df, "is_dead", 
   n.yes.train = n_yes*0.8, 
   n.no.train = n_no*0.8,
   resampling = F,
   reps = nrep,
   method = "glmnet")
}

saveRDS(exp_I_saps_list_add, paste0("prediction_experiments/exp_I_saps_list_add.RDS"))

Blood trajectory + summary stats + BL feature set (with SAPSII)

Not run during knitting

exp_I_saps_list_add_saps <- list()

for (cutoff in c(1, 3, 7, 14, 21)){
  
  cat("Exp I, cutoff:", cutoff, "\n")
  predict_list <- readRDS(paste0("prediction_experiments/predict_list_taskI_II_", cutoff,
                               "days.Rds"))
  
  predict_traj_df <- do.call(rbind,predict_list) %>%
    left_join(subject_id_df) %>%
    right_join(mimic_patients_all_saps) %>%
    filter(!analyte %in% c("Bicarbonate", "Calcium, Total", "Creatinine")) %>%
    select(prob1, prob2, prob3, analyte, subject_id, hospital_expire_flag) %>%
    mutate(is_dead = ifelse(hospital_expire_flag, "yes", "no"))%>%
    pivot_wider(id_cols =c(subject_id, is_dead),
                names_from = analyte,
                values_from = c(prob1, prob2, prob3))
  
  predict_traj_df <- predict_traj_df %>%
    select(-all_of(names(which(sapply(predict_traj_df, 
                                      function(x) mean(is.na(x)))==1))))
  
  predict_raw_df <- mimic_modeling_set_f %>%
    filter(subject_id%in%mimic_patients_all_saps$subject_id)%>%
    filter(time_days <= cutoff) %>%
    select(subject_id,label, value_clean) %>%
    group_by(subject_id, label) %>%
    summarise(mean = mean(value_clean, na.rm = T),
              sd = sd(value_clean, na.rm = T),
              max = max(value_clean, na.rm = T),
              min = min(value_clean, na.rm = T)) %>%
    pivot_wider(id_cols =subject_id,
                names_from = label,
                values_from = c(mean, sd, max, min))
  
  predict_df <- predict_traj_df %>%
    left_join(predict_raw_df)
  
  # Mean impute remaining missing values
  predict_df[3:ncol(predict_df)] <- lapply(predict_df[3:ncol(predict_df)], function(x) {
    x[is.na(x)] <- mean(x, na.rm = TRUE)
    return(x)
  })
  
  predict_df <- predict_df %>%
    right_join(mimic_patients_all_saps %>% 
                select(subject_id, cohort_group, age, gender, insurance, ethnicity, sapsii))%>%
    filter(complete.cases(.))
  
  n_yes <- table(predict_df$is_dead)[2]
  n_no <- table(predict_df$is_dead)[1]
  
  exp_I_saps_list_add_saps[[as.character(cutoff)]] <- run_experiment(
   predict_df, "is_dead", 
   n.yes.train = n_yes*0.8, 
   n.no.train = n_no*0.8,
   resampling = F,
   reps = nrep,
   method = "glmnet")
}

saveRDS(exp_I_saps_list_add_saps, paste0("prediction_experiments/exp_I_saps_list_add_saps.RDS"))

Summary performance

## blood only
# Load results of prediction experiments
exp_I_saps_list <- readRDS(paste0("prediction_experiments/exp_I_saps_list_saps.RDS"))

# Drop any NULL entries (failed experiments)
exp_I_saps_list <- exp_I_saps_list[which(sapply(exp_I_saps_list, function(x) !is.null(x)))]
exp_I_saps_df <- experiment_performance(exp_I_saps_list, nrep)

## blood + summary stats
# Load results of prediction experiments
exp_I_saps_list_trj <- readRDS(paste0("prediction_experiments/exp_I_saps_list_trj.RDS"))

# Drop any NULL entries (failed experiments)
exp_I_saps_list_trj <- exp_I_saps_list_trj[which(sapply(exp_I_saps_list_trj, function(x) !is.null(x)))]

# Extract and reshape performance metrics across all cutoff experiments
exp_I_saps_list_trj_df <- experiment_performance(exp_I_saps_list_trj,nrep)

## blood + summary stats +bl
# Load results of prediction experiments
exp_I_saps_list_both <- readRDS(paste0("prediction_experiments/exp_I_saps_list_both.RDS"))

# Drop any NULL entries (failed experiments)
exp_I_saps_list_both <- exp_I_add_list[which(sapply(exp_I_saps_list_both, function(x) !is.null(x)))]
exp_I_saps_list_both_df <- experiment_performance(exp_I_saps_list_both,nrep)

# Load results of prediction experiments
exp_I_saps_list_add <- readRDS(paste0("prediction_experiments/exp_I_saps_list_add.RDS"))

# Drop any NULL entries (failed experiments)
exp_I_saps_list_add  <- exp_I_saps_list_add [which(sapply(exp_I_saps_list_add , function(x) !is.null(x)))]

exp_I_saps_list_add_df <- experiment_performance(exp_I_saps_list_add ,nrep)


# Load results of prediction experiments
exp_I_saps_list_add_saps <- readRDS(paste0("prediction_experiments/exp_I_saps_list_add_saps.RDS"))

# Drop any NULL entries (failed experiments)
exp_I_saps_list_add_saps <- exp_I_saps_list_add_saps[which(sapply(exp_I_saps_list_add_saps, function(x) !is.null(x)))]
exp_I_saps_list_add_saps_df <- experiment_performance(exp_I_saps_list_add_saps, nrep)

#Join datasets
exp_I_saps_df$feature_list<-"SAPSII"
exp_I_saps_list_trj_df$feature_list <- "Traj. PPA"
exp_I_saps_list_both_df$feature_list <- "Traj. PPA + Sum. stats"
exp_I_saps_list_add_df$feature_list <- "Traj. PPA + Sum. stats + BL"
exp_I_saps_list_add_saps_df$feature_list <- "Traj. PPA + Sum. stats + BL + SAPSII"

exp_I_saps_df <- bind_rows(exp_I_saps_df, exp_I_saps_list_trj_df, exp_I_saps_list_both_df,
                           exp_I_saps_list_add_df, exp_I_saps_list_add_saps_df)

saveRDS(exp_I_saps_df, paste0("prediction_experiments/exp_I_saps_df.RDS"))

Hypothesis testing

## Across feature list
f_result_featureList_I <- list()

for (c in c(1, 3, 7, 14, 21)){
  x <- exp_I_df %>%
    filter(cutoff == c) %>%
    group_by(feature_list) %>%
    mutate(block = 1:n())
  
  f_result <- friedman.test(x$pr_auc, x$feature_list, x$block)
  
  f_result_featureList_I[[c]] <- data.frame(
    cutoff = c,
    chi_squared = f_result$statistic,
    p_value = sprintf("%.5f", f_result$p.value)
  )
}

f_result_featureList_I_df <- bind_rows(f_result_featureList_I)

## Across time for each feature list
f_result_cutoff_I <- list()

for (f in unique(exp_I_df$feature_list)){
  x <- exp_I_df %>% 
    filter(feature_list == f) %>%
    group_by(cutoff) %>%
    mutate(block = 1:n())
  
  f_result <- friedman.test(x$pr_auc, x$cutoff, x$block)
  
  f_result_cutoff_I[[f]] <- data.frame(
    feature_list = f,
    chi_squared = f_result$statistic,
    p_value = sprintf("%.5f", f_result$p.value)
  )
}

f_result_cutoff_I_df <- bind_rows(f_result_cutoff_I)

kable(f_result_featureList_I_df, caption = "Friedman Test Results by Cutoff") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

Friedman Test Results by Cutoff
	cutoff	chi_squared
Friedman chi-squared…1	1	49.24
Friedman chi-squared…2	3	61.00
Friedman chi-squared…3	7	48.16
Friedman chi-squared…4	14	71.08
Friedman chi-squared…5	21	69.12

kable(f_result_cutoff_I_df, caption = "Friedman Test Results by Feature List") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

Friedman Test Results by Feature List
	feature_list	chi_squared
Friedman chi-squared…1	Traj. PPA	91.248
Friedman chi-squared…2	Traj. PPA + Sum. stats	136.160
Friedman chi-squared…3	Traj. PPA + Sum. stats + BL	86.960

Figures Exp I

PR-AUC

.colors = c("#A3A500","#F8766D","#00BA38","#619CFF","#E76BF3")

# Out-train performance figure PR-AUC
task_I_saps_out_train_pr_plot <- exp_I_saps_df %>%
  filter(per_type == "out-train") %>%
  mutate(cutoff = factor(cutoff, levels = c(1, 3, 7, 14, 21),
                         labels = c("<= 1 days", "<= 3 days",
                                    "<= 7 days","<= 14 days","<= 21 days"))) %>%
  ggplot(aes(feature_list, pr_auc, fill = feature_list))+
  geom_boxplot(linewidth = 0.1)+
  geom_hline(aes(yintercept = mean(pr_BL)), color = "red", linetype = "dashed")+
   scale_fill_manual(values = .colors)+
  facet_grid(~cutoff)+
  ylim(0,1)+
  ylab("PR-AUC (out-train)")+
  theme_bw()+
  theme(axis.text.x = element_blank(), axis.title.x = element_blank(),
        legend.position = "bottom", legend.title = element_blank())

task_I_saps_out_train_pr_plot

# In-train performance figure PR-AUC
task_I_saps_in_train_pr_plot <- exp_I_saps_df %>%
  filter(per_type == "in-train") %>%
  mutate(cutoff = factor(cutoff, levels = c(1, 3, 7, 14, 21),
                         labels = c("<= 1 days", "<= 3 days",
                                    "<= 7 days","<= 14 days","<= 21 days"))) %>%
  ggplot(aes(feature_list, pr_auc, fill = feature_list))+
  geom_boxplot(linewidth = 0.1)+
  geom_hline(aes(yintercept = mean(pr_BL)), color = "red", linetype = "dashed")+
   scale_fill_manual(values = .colors)+
  facet_grid(~cutoff)+
  ylim(0,1)+
  ylab("PR-AUC (in-train)")+
  theme_bw()+
  theme(axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        legend.position = "bottom",
        legend.title = element_blank())

task_I_saps_in_train_pr_plot

ggsave(plot = task_I_saps_out_train_pr_plot,
       filename = paste0("figures/Task I PR-AUC_saps.png"), 
       width = 9,
       height = 5)

ggsave(plot = task_I_saps_in_train_pr_plot,
       filename = paste0("figures/Task I PR-AUC in-train_saps.png"), 
       width = 9,
       height = 5)

# Out-train performance figure ROC-AUC
task_I_saps_out_train_roc_plot <- exp_I_saps_df %>%
  filter(per_type == "out-train") %>%
  mutate(cutoff = factor(cutoff, levels = c(1, 3, 7, 14, 21),
                         labels = c("<= 1 days", "<= 3 days",
                                    "<= 7 days","<= 14 days","<= 21 days"))) %>%
  ggplot(aes(feature_list, roc_auc, fill = feature_list))+
  geom_boxplot(linewidth = 0.1)+
  geom_hline(yintercept = 0.5, color = "red", linetype = "dashed")+
   scale_fill_manual(values = .colors)+
  facet_grid(~cutoff)+
  ylim(0.5,1)+
  ylab("ROC-AUC (out-train)")+
  theme_bw()+
  theme(axis.text.x = element_blank(), axis.title.x = element_blank(),
        legend.position = "bottom", legend.title = element_blank())

task_I_saps_out_train_roc_plot

# In-train performance figure ROC-AUC
task_I_saps_in_train_roc_plot <- exp_I_saps_df%>%
  filter(per_type == "in-train") %>%
  mutate(cutoff = factor(cutoff, levels = c(1, 3, 7, 14, 21),
                         labels = c("<= 1 days", "<= 3 days",
                                    "<= 7 days","<= 14 days","<= 21 days"))) %>%
  ggplot(aes(feature_list, roc_auc, fill = feature_list))+
  geom_boxplot(linewidth = 0.1)+
  geom_hline(yintercept = 0.5, color = "red", linetype = "dashed")+
   scale_fill_manual(values = .colors)+
  facet_grid(~cutoff)+
  ylim(0.5,1)+
  ylab("ROC-AUC (in-train)")+
  theme_bw()+
  theme(axis.text.x = element_blank(), axis.title.x = element_blank(),
        legend.position = "bottom", legend.title = element_blank())

task_I_saps_in_train_roc_plot

ggsave(plot = task_I_saps_out_train_roc_plot,
       filename = paste0("figures/Task I ROC-AUC_saps.png"), 
       width = 9,
       height = 5)

ggsave(plot = task_I_saps_in_train_roc_plot,
       filename = paste0("figures/Task I ROC-AUC in-train_saps.png"), 
       width = 9,
       height = 5)

SAPSII Experiment II

SAPSII alone

exp_II_saps_list <- list()

for (cutoff in c(1, 3, 7, 14, 21)){
  cat("Exp I, cutoff:", cutoff, "\n")
  
  predict_list <- readRDS(
    paste0("prediction_experiments/predict_list_taskI_II_",
           cutoff,
           "days.Rds")
  )
  
  predict_df<-  subject_id_df %>%
    left_join(mimic_patients_all_saps) %>%
    select(subject_id, cohort_group, sapsii) %>%
    filter(cohort_group != "SCI_noFracture") %>%
    mutate(is_SCI = ifelse(cohort_group == "SCI_Fracture", "yes", "no")) %>%
    select(subject_id, is_SCI, sapsii) %>%
    filter(complete.cases(.)) 

  # Mean impute remaining missing values
  predict_df[3:ncol(predict_df)] <- lapply(predict_df[3:ncol(predict_df)], function(x) {
    x[is.na(x)] <- mean(x, na.rm = TRUE)
    return(x)
  })
  
  n_yes <- table(predict_df$is_SCI)[2]
  n_no <- table(predict_df$is_SCI)[1]
  
  exp_II_saps_list[[as.character(cutoff)]] <- run_experiment(
    predict_df, 
    "is_SCI", 
    n.yes.train = n_yes*0.8, 
    n.no.train = n_no*0.8,
    resampling = F,
    method = "lr",
    reps = nrep)
}

saveRDS(exp_II_saps_list, "prediction_experiments/exp_II_saps_list_saps.RDS")

Blood trajectory feature set

# Run mortality prediction experiments for different time cutoffs
# This may take time depending on model complexity and number of repetitions
exp_II_saps_list_trj <- list()

for (cutoff in c(1, 3, 7, 14, 21)) {
  
  cat("Exp I, cutoff:", cutoff, "\n")
  
  # Load predictions for a given cutoff
  predict_list <- readRDS(paste0("prediction_experiments/predict_list_taskI_II_", cutoff, "days.Rds"))
  
  # Combine and enrich prediction data
  predict_df <- do.call(rbind, predict_list) %>%
    left_join(subject_id_df) %>%
    left_join(mimic_patients_all_saps) %>%
    filter(!analyte %in% c("Bicarbonate", "Calcium, Total", "Creatinine")) %>%
    select(prob1, prob2, prob3, analyte, subject_id, cohort_group) %>%
    filter(cohort_group != "SCI_noFracture") %>%
    mutate(is_SCI = ifelse(cohort_group == "SCI_Fracture", "yes", "no")) %>%
    pivot_wider(
      id_cols = c(subject_id, is_SCI),
      names_from = analyte,
      values_from = c(prob1, prob2, prob3)
    )
  
  # Drop columns with all missing values
  predict_df <- predict_df %>%
    select(-all_of(names(which(sapply(predict_df, function(x) mean(is.na(x))) == 1))))
  
  # Mean impute remaining missing values
  predict_df[3:ncol(predict_df)] <- lapply(predict_df[3:ncol(predict_df)], function(x) {
    x[is.na(x)] <- mean(x, na.rm = TRUE)
    return(x)
  })
  
  # Determine class counts
  n_yes <- table(predict_df$is_SCI)[["yes"]]
  n_no  <- table(predict_df$is_SCI)[["no"]]
  
  # Run predictive model experiment
  exp_II_saps_list_trj[[as.character(cutoff)]] <- run_experiment(
    predict_df,
    target.name = "is_SCI",
    n.yes.train = n_yes * 0.8,
    n.no.train  = n_no * 0.8,
    resampling = F,
    reps = nrep,
    method = "glmnet"
  )
}

# Save the result of all experiments
saveRDS(exp_II_saps_list_trj, paste0("prediction_experiments/exp_II_saps_list_trj.Rds"))

Blood trajectory + summary stats feature set

Not run during knitting

exp_II_saps_list_both <- list()

for (cutoff in c(1,3, 7, 14 ,21)){
  cat("Exp I raw, cutoff:", cutoff, "\n")
  
  predict_list <- readRDS(paste0("prediction_experiments/predict_list_taskI_II_",
                                 cutoff,
                                 "days.Rds"))
  
  predict_traj_df <- do.call(rbind,predict_list) %>%
    left_join(subject_id_df) %>%
    left_join(mimic_patients_all_saps) %>%
    filter(!analyte %in% c("Bicarbonate", "Calcium, Total", "Creatinine")) %>%
    select(prob1, prob2, prob3, analyte, subject_id, cohort_group) %>%
    filter(cohort_group != "SCI_noFracture") %>%
    mutate(is_SCI = ifelse(cohort_group == "SCI_Fracture", "yes", "no")) %>%
    pivot_wider(id_cols = c(subject_id, is_SCI),
                names_from = analyte, values_from = c(prob1, prob2, prob3))
  
  predict_traj_df <- predict_traj_df %>%
    select(-all_of(names(which(sapply(predict_traj_df, 
                                      function(x) mean(is.na(x)))==1))))
  
  predict_raw_df <- mimic_modeling_set_f %>%
    filter(time_days <= cutoff) %>%
    select(subject_id,label, value_clean) %>%
    group_by(subject_id, label) %>%
    summarise(mean = mean(value_clean, na.rm = T),
              sd = sd(value_clean, na.rm = T),
              max = max(value_clean, na.rm = T),
              min = min(value_clean, na.rm = T)) %>%
    pivot_wider(id_cols = subject_id, names_from = label,
                values_from = c(mean, sd, max, min)) %>%
    right_join(mimic_patients_all_saps %>%
                select(subject_id, hospital_expire_flag)) %>%
    mutate(is_SCI = ifelse(hospital_expire_flag == 1, "yes", "no")) %>%
    relocate(is_SCI, .after = subject_id) %>%
    select(-hospital_expire_flag)
  
  
  predict_df <- predict_traj_df %>%
    right_join(predict_raw_df)
  
  # Mean impute remaining missing values
  predict_df[3:ncol(predict_df)] <- lapply(predict_df[3:ncol(predict_df)], function(x) {
    x[is.na(x)] <- mean(x, na.rm = TRUE)
    return(x)
  })
  
 n_yes <- table(predict_df$is_SCI)[["yes"]]
 n_no <- table(predict_df$is_SCI)[["no"]]
  
 exp_II_saps_list_both[[as.character(cutoff)]] <- run_experiment(
  predict_df, "is_SCI", 
  n.yes.train = n_yes*0.8, 
  n.no.train = n_no*0.8,
  resampling = F,
  reps = nrep,
  method = "glmnet")
}

saveRDS(exp_II_saps_list_both , paste0("prediction_experiments/exp_II_saps_list_both.RDS"))

Blood trajectory + summary stats + BL feature set

Not run during knitting

exp_II_saps_list_add <- list()

for (cutoff in c(1, 3, 7, 14, 21)){
  
  cat("Exp I, cutoff:", cutoff, "\n")
  predict_list <- readRDS(paste0("prediction_experiments/predict_list_taskI_II_", cutoff,
                               "days.Rds"))
  
  predict_traj_df <- do.call(rbind,predict_list) %>%
    left_join(subject_id_df) %>%
    right_join(mimic_patients_all_saps) %>%
    filter(!analyte %in% c("Bicarbonate", "Calcium, Total", "Creatinine")) %>%
    select(prob1, prob2, prob3, analyte, subject_id, cohort_group) %>%
    filter(cohort_group != "SCI_noFracture") %>%
    mutate(is_SCI = ifelse(cohort_group == "SCI_Fracture", "yes", "no")) %>%
    pivot_wider(id_cols =c(subject_id, is_SCI),
                names_from = analyte,
                values_from = c(prob1, prob2, prob3))
  
  predict_traj_df <- predict_traj_df %>%
    select(-all_of(names(which(sapply(predict_traj_df, 
                                      function(x) mean(is.na(x)))==1))))
  
  predict_raw_df <- mimic_modeling_set_f %>%
    filter(subject_id%in%mimic_patients_all_saps$subject_id)%>%
    filter(time_days <= cutoff) %>%
    select(subject_id,label, value_clean) %>%
    group_by(subject_id, label) %>%
    summarise(mean = mean(value_clean, na.rm = T),
              sd = sd(value_clean, na.rm = T),
              max = max(value_clean, na.rm = T),
              min = min(value_clean, na.rm = T)) %>%
    pivot_wider(id_cols =subject_id,
                names_from = label,
                values_from = c(mean, sd, max, min))
  
  predict_df <- predict_traj_df %>%
    left_join(predict_raw_df)
  
  # Mean impute remaining missing values
  predict_df[3:ncol(predict_df)] <- lapply(predict_df[3:ncol(predict_df)], function(x) {
    x[is.na(x)] <- mean(x, na.rm = TRUE)
    return(x)
  })
  
  predict_df <- predict_df %>%
    right_join(mimic_patients_all %>% 
                select(subject_id, age, gender, insurance, ethnicity))%>%
    filter(complete.cases(.))
  
  n_yes <- table(predict_df$is_SCI)[2]
  n_no <- table(predict_df$is_SCI)[1]
  
  exp_II_saps_list_add[[as.character(cutoff)]] <- run_experiment(
   predict_df, "is_SCI", 
   n.yes.train = n_yes*0.8, 
   n.no.train = n_no*0.8,
   resampling = F,
   reps = nrep,
   method = "glmnet")
}

saveRDS(exp_II_saps_list_add, paste0("prediction_experiments/exp_II_saps_list_add.RDS"))

Blood trajectory + summary stats + BL feature set (with SAPS)

Not run during knitting

exp_II_saps_list_add_saps <- list()

for (cutoff in c(1, 3, 7, 14, 21)){
  
  cat("Exp I, cutoff:", cutoff, "\n")
  predict_list <- readRDS(paste0("prediction_experiments/predict_list_taskI_II_", cutoff,
                               "days.Rds"))
  
  predict_traj_df <- do.call(rbind,predict_list) %>%
    left_join(subject_id_df) %>%
    right_join(mimic_patients_all_saps) %>%
    filter(!analyte %in% c("Bicarbonate", "Calcium, Total", "Creatinine")) %>%
    select(prob1, prob2, prob3, analyte, subject_id, cohort_group) %>%
    filter(cohort_group != "SCI_noFracture") %>%
    mutate(is_SCI = ifelse(cohort_group == "SCI_Fracture", "yes", "no")) %>%
    pivot_wider(id_cols =c(subject_id, is_SCI),
                names_from = analyte,
                values_from = c(prob1, prob2, prob3))
  
  predict_traj_df <- predict_traj_df %>%
    select(-all_of(names(which(sapply(predict_traj_df, 
                                      function(x) mean(is.na(x)))==1))))
  
  predict_raw_df <- mimic_modeling_set_f %>%
    filter(subject_id%in%mimic_patients_all_saps$subject_id)%>%
    filter(time_days <= cutoff) %>%
    select(subject_id,label, value_clean) %>%
    group_by(subject_id, label) %>%
    summarise(mean = mean(value_clean, na.rm = T),
              sd = sd(value_clean, na.rm = T),
              max = max(value_clean, na.rm = T),
              min = min(value_clean, na.rm = T)) %>%
    pivot_wider(id_cols =subject_id,
                names_from = label,
                values_from = c(mean, sd, max, min))
  
  predict_df <- predict_traj_df %>%
    left_join(predict_raw_df)
  
  # Mean impute remaining missing values
  predict_df[3:ncol(predict_df)] <- lapply(predict_df[3:ncol(predict_df)], function(x) {
    x[is.na(x)] <- mean(x, na.rm = TRUE)
    return(x)
  })
  
  predict_df <- predict_df %>%
    right_join(mimic_patients_all_saps %>% 
                select(subject_id, age, gender, insurance, ethnicity, sapsii))%>%
    filter(complete.cases(.))
  
  n_yes <- table(predict_df$is_SCI)[2]
  n_no <- table(predict_df$is_SCI)[1]
  
  exp_II_saps_list_add_saps[[as.character(cutoff)]] <- run_experiment(
   predict_df, "is_SCI", 
   n.yes.train = n_yes*0.8, 
   n.no.train = n_no*0.8,
   resampling = F,
   reps = nrep,
   method = "glmnet")
}

saveRDS(exp_II_saps_list_add_saps, paste0("prediction_experiments/exp_II_saps_list_add_saps.RDS"))

Summary performance

## blood only
# Load results of prediction experiments
exp_II_saps_list <- readRDS(paste0("prediction_experiments/exp_II_saps_list_saps.RDS"))

# Drop any NULL entries (failed experiments)
exp_II_saps_list <- exp_II_saps_list[which(sapply(exp_II_saps_list, function(x) !is.null(x)))]
exp_II_saps_df <- experiment_performance(exp_II_saps_list, nrep)

## blood + summary stats
# Load results of prediction experiments
exp_II_saps_list_trj <- readRDS(paste0("prediction_experiments/exp_II_saps_list_trj.RDS"))

# Drop any NULL entries (failed experiments)
exp_II_saps_list_trj <- exp_II_saps_list_trj[which(sapply(exp_II_saps_list_trj, function(x) !is.null(x)))]

# Extract and reshape performance metrics across all cutoff experiments
exp_II_saps_list_trj_df <- experiment_performance(exp_II_saps_list_trj,nrep)

## blood + summary stats +bl
# Load results of prediction experiments
exp_II_saps_list_both <- readRDS(paste0("prediction_experiments/exp_II_saps_list_both.RDS"))

# Drop any NULL entries (failed experiments)
exp_II_saps_list_both <- exp_II_add_list[which(sapply(exp_II_saps_list_both, function(x) !is.null(x)))]
exp_II_saps_list_both_df <- experiment_performance(exp_II_saps_list_both,nrep)

# Load results of prediction experiments
exp_II_saps_list_add <- readRDS(paste0("prediction_experiments/exp_II_saps_list_add.RDS"))

# Drop any NULL entries (failed experiments)
exp_II_saps_list_add  <- exp_II_saps_list_add [which(sapply(exp_II_saps_list_add , function(x) !is.null(x)))]

exp_II_saps_list_add_df <- experiment_performance(exp_II_saps_list_add ,nrep)

# Load results of prediction experiments
exp_II_saps_list_add_saps <- readRDS(paste0("prediction_experiments/exp_II_saps_list_add_saps.RDS"))

# Drop any NULL entries (failed experiments)
exp_II_saps_list_add_saps <- exp_II_saps_list_add_saps[which(sapply(exp_II_saps_list_add_saps, function(x) !is.null(x)))]
exp_II_saps_list_add_saps_df <- experiment_performance(exp_II_saps_list_add_saps, nrep)

#Join datasets
exp_II_saps_df$feature_list<-"SAPSII"
exp_II_saps_list_trj_df$feature_list <- "Traj. PPA"
exp_II_saps_list_both_df$feature_list <- "Traj. PPA + Sum. stats"
exp_II_saps_list_add_df$feature_list <- "Traj. PPA + Sum. stats + BL"
exp_II_saps_list_add_saps_df$feature_list <- "Traj. PPA + Sum. stats + BL + SAPSII"

exp_II_saps_df <- bind_rows(exp_II_saps_df, exp_II_saps_list_trj_df, exp_II_saps_list_both_df,
                           exp_II_saps_list_add_df, exp_II_saps_list_add_saps_df)

saveRDS(exp_II_saps_df, paste0("prediction_experiments/exp_II_saps_df.RDS"))

Hypothesis testing

## Across feature list
f_result_featureList_II <- list()

for (c in c(1, 3, 7, 14, 21)){
  x <- exp_II_df %>%
    filter(cutoff == c) %>%
    group_by(feature_list) %>%
    mutate(block = 1:n())
  
  f_result <- friedman.test(x$pr_auc, x$feature_list, x$block)
  
  f_result_featureList_II[[c]] <- data.frame(
    cutoff = c,
    chi_squared = f_result$statistic,
    p_value = sprintf("%.7f", f_result$p.value)
  )
}

f_result_featureList_II_df <- bind_rows(f_result_featureList_II)

## Across time for each feature list

f_result_cutoff_II <- list()

for (f in unique(exp_II_df$feature_list)){
  x <- exp_II_df %>%
    filter(feature_list == f) %>%
    group_by(cutoff) %>%
    mutate(block = 1:n())
  
  f_result <- friedman.test(x$pr_auc, x$cutoff, x$block)
  
  f_result_cutoff_II[[f]] <- data.frame(
    feature_list = f,
    chi_squared = f_result$statistic,
    p_value = sprintf("%.7f", f_result$p.value)
  )
}

f_result_cutoff_II_df <- bind_rows(f_result_cutoff_II)

kable(f_result_featureList_II_df, caption = "Friedman Test Results by Cutoff") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

Friedman Test Results by Cutoff
	cutoff	chi_squared
Friedman chi-squared…1	1	48.52
Friedman chi-squared…2	3	72.84
Friedman chi-squared…3	7	52.96
Friedman chi-squared…4	14	62.44
Friedman chi-squared…5	21	45.72

kable(f_result_cutoff_II_df, caption = "Friedman Test Results by Feature List") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

Friedman Test Results by Feature List
	feature_list	chi_squared
Friedman chi-squared…1	Traj. PPA	134.672
Friedman chi-squared…2	Traj. PPA + Sum. stats	123.792
Friedman chi-squared…3	Traj. PPA + Sum. stats + BL	108.272

Figure Exp II

task_II_saps_out_train_pr_plot <- exp_II_saps_df %>%
  filter(per_type == "out-train") %>%
  mutate(cutoff = factor(cutoff, levels = c(1, 3, 7, 14, 21),
                         labels = c("<= 1 days", "<= 3 days",
                                    "<= 7 days","<= 14 days","<= 21 days"))) %>%
  ggplot(aes(feature_list, pr_auc, fill = feature_list))+
  geom_boxplot(linewidth = 0.1)+
  geom_hline(aes(yintercept = mean(pr_BL)), color = "red", linetype = "dashed")+
   scale_fill_manual(values = .colors)+
  facet_grid(~cutoff)+
  ylim(0,1)+
  theme_bw()+
  ylab("PR-AUC (out-train)")+
  theme(axis.text.x = element_blank(), 
        axis.title.x = element_blank(),
        legend.position = "bottom", 
        legend.title = element_blank())

task_II_saps_out_train_pr_plot

task_II_saps_in_train_pr_plot <- exp_II_saps_df %>%
  filter(per_type == "in-train") %>%
  mutate(cutoff = factor(cutoff, levels = c(1, 3, 7, 14, 21),
                         labels = c("<= 1 days", "<= 3 days",
                                    "<= 7 days","<= 14 days","<= 21 days"))) %>%
  ggplot(aes(feature_list, pr_auc, fill = feature_list))+
  geom_boxplot(linewidth = 0.1)+
  geom_hline(aes(yintercept = mean(pr_BL)), color = "red", linetype = "dashed")+
   scale_fill_manual(values = .colors)+
  facet_grid(~cutoff)+
  ylim(0,1)+
  ylab("PR-AUC (in-train)")+
  theme_bw()+
  theme(axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        legend.position = "bottom",
        legend.title = element_blank())

task_II_saps_in_train_pr_plot

ggsave(plot = task_II_saps_out_train_pr_plot,
       filename = paste0("figures/Task II PR-AUC out_train_saps.png"), 
       width = 9,
       height = 5)

ggsave(plot = task_II_saps_in_train_pr_plot,
       filename = paste0("figures/Task II PR-AUC in_train_saps.png"), 
       width = 9,
       height = 5)

task_II_saps_out_train_roc_plot <- exp_II_saps_df %>%
  filter(per_type == "out-train") %>%
  mutate(cutoff = factor(cutoff, levels = c(1, 3, 7, 14, 21),
                         labels = c("<= 1 days", "<= 3 days",
                                    "<= 7 days","<= 14 days","<= 21 days"))) %>%
  ggplot(aes(feature_list, roc_auc, fill = feature_list))+
  geom_boxplot(linewidth = 0.1)+
  geom_hline(yintercept = 0.5, color = "red", linetype = "dashed")+
   scale_fill_manual(values = .colors)+
  facet_grid(~ cutoff)+
  ylim(0.5,1)+
  theme_bw()+
  ylab("ROC-AUC (out-train)")+
  theme(axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        legend.position = "bottom",
        legend.title = element_blank())

task_II_saps_out_train_roc_plot

task_II_saps_in_train_roc_plot <- exp_II_saps_df %>%
  filter(per_type == "in-train") %>%
  mutate(cutoff = factor(cutoff, levels = c(1, 3, 7, 14, 21),
                         labels = c("<= 1 days", "<= 3 days",
                                    "<= 7 days","<= 14 days","<= 21 days"))) %>%
  ggplot(aes(feature_list, roc_auc, fill = feature_list))+
  geom_boxplot(linewidth = 0.1)+
  geom_hline(yintercept = 0.5, color = "red", linetype = "dashed")+
   scale_fill_manual(values = .colors)+
  facet_grid(~cutoff)+
  ylim(0.5,1)+
  ylab("ROC-AUC (in-train)")+
  theme_bw()+
  theme(axis.text.x = element_blank(),
        axis.title.x = element_blank(),
        legend.position = "bottom",
        legend.title = element_blank())

task_II_saps_in_train_roc_plot

ggsave(plot = task_II_saps_out_train_roc_plot,
       filename = paste0("figures/Task II ROC-AUC out_train_saps.png"), 
       width = 9,
       height = 5)

ggsave(plot = task_II_saps_in_train_roc_plot,
       filename = paste0("figures/Task II ROC-AUC in_train_saps.png"), 
       width = 9,
       height = 5)

Tables and Figures SAPSII Experiments

Tables

exp_I_saps_df$Experiment<-"Exp. I"
exp_II_saps_df$Experiment<-"Exp. II"

exp_saps_df<-bind_rows(exp_I_saps_df, exp_II_saps_df)

exp_saps_summary_df<-exp_saps_df%>%
  group_by(Experiment, per_type, cutoff, feature_list)%>%
  summarise(mean_roc_auc = mean(roc_auc),
            se_roc_auc = sd(roc_auc)/sqrt(n()),
            low_ci_roc_auc = mean_roc_auc - 1.96*se_roc_auc,
            high_ci_roc_auc = mean_roc_auc + 1.96*se_roc_auc,
            mean_pr_auc = mean(pr_auc),
            se_pr_auc = sd(pr_auc)/sqrt(n()),
            low_ci_pr_auc = mean_pr_auc - 1.96*se_pr_auc,
            high_ci_pr_auc = mean_pr_auc + 1.96*se_pr_auc,
            mean_pr_BL = mean(pr_BL),
            # text summary for printing
            `ROC AUC` = paste0(
              sprintf("%.2f", mean_roc_auc),
              " [", sprintf("%.2f", low_ci_roc_auc), "-", 
              sprintf("%.2f", high_ci_roc_auc), "]"),
            `PR AUC` = paste0(
              sprintf("%.2f", mean_pr_auc),
              " [", sprintf("%.2f", low_ci_pr_auc), "-", 
              sprintf("%.2f", high_ci_pr_auc), "]"),
            Prevalence = sprintf("%.2f", mean_pr_BL))%>%
  pivot_wider(id_cols = c(per_type, Experiment, feature_list), names_from = c(cutoff), values_from = c(`ROC AUC`, `PR AUC`, Prevalence))

##roc_table_saps
exp_saps_summary_df%>%
  ungroup()%>%
  filter(per_type =="out-train")%>%
  select(Experiment, feature_list, paste0("ROC AUC_", c("1", "3", "7", "14", "21")))%>%
  kableExtra::kbl(row.names = F)%>%
  kable_styling(full_width = F)%>%
  kable_classic()%>%
  kableExtra::save_kable(file = "tables/exp_saps_ROC_out_train.html")

##roc_table_saps
exp_saps_summary_df%>%
  ungroup()%>%
  filter(per_type =="in-train")%>%
  select(Experiment, feature_list, paste0("ROC AUC_", c("1", "3", "7", "14", "21")))%>%
  kableExtra::kbl(row.names = F)%>%
  kable_styling(full_width = F)%>%
  kable_classic()%>%
  kableExtra::save_kable(file = "tables/exp_saps_ROC_in_train.html")

##PR_table_saps
exp_saps_summary_df%>%
  ungroup()%>%
  filter(per_type =="out-train")%>%
  select(Experiment, feature_list, paste0(c("PR AUC_", "Prevalence_"), 
                                          rep(c("1", "3", "7", "14", "21"), each = 2)))%>%
  kableExtra::kbl(row.names = F)%>%
  kable_styling(full_width = F)%>%
  kable_classic()%>%
  kableExtra::save_kable(file = "tables/exp_saps_PR_out_train.html")

##PR_table_saps
exp_saps_summary_df%>%
  ungroup()%>%
  filter(per_type =="in-train")%>%
  select(Experiment, feature_list, paste0(c("PR AUC_", "Prevalence_"), 
                                          rep(c("1", "3", "7", "14", "21"), each = 2)))%>%
  kableExtra::kbl(row.names = F)%>%
  kable_styling(full_width = F)%>%
  kable_classic()%>%
  kableExtra::save_kable(file = "tables/exp_saps_PR_in_train.html")

Figures

roc_saps_out_train_fig<-
  task_I_saps_out_train_roc_plot+
  task_II_saps_out_train_roc_plot+
  plot_layout(ncol = 1)+
  plot_annotation(tag_levels = "a")

roc_saps_out_train_fig

ggsave(plot = roc_saps_out_train_fig, "figures/ROC_saps_out_train_fig.png", height = 8, width = 6)

roc_saps_in_train_fig<-
  task_I_saps_in_train_roc_plot+
  task_II_saps_in_train_roc_plot+
  plot_layout(ncol = 1)+
  plot_annotation(tag_levels = "a")

roc_saps_in_train_fig

ggsave(plot = roc_saps_in_train_fig, "figures/ROC_saps_in_train_fig.png", height = 8, width = 6)

pr_saps_out_train_fig<-
  task_I_out_train_pr_plot+
  task_II_out_train_pr_plot+
  plot_layout(ncol = 1)+
  plot_annotation(tag_levels = "a")

pr_saps_out_train_fig

ggsave(plot = pr_saps_out_train_fig, "figures/PR_saps_out_train_fig.png", height = 8, width = 6)

pr_saps_in_train_fig<-
  task_I_saps_in_train_pr_plot+
  task_II_saps_in_train_pr_plot+
  plot_layout(ncol = 1)+
  plot_annotation(tag_levels = "a")

pr_saps_in_train_fig

ggsave(plot = pr_saps_in_train_fig, "figures/PR_saps_in_train_fig.png", height = 8, width = 6)

Variable Importance plots

exp_I_list <- readRDS("prediction_experiments/exp_I_list_no_balance.Rds")
exp_I_both_list <- readRDS("prediction_experiments/exp_I_both_list_no_balance.RDS")
exp_I_add_list <- readRDS("prediction_experiments/exp_I_add_list_no_balance.RDS")

exp_II_list <-readRDS("prediction_experiments/exp_II_list_no_balance.RDS")
exp_II_both_list <- readRDS("prediction_experiments/exp_II_both_list_no_balance.RDS")
exp_II_add_list <- readRDS("prediction_experiments/exp_II_add_list_no_balance.RDS")

exp_III_list <- readRDS("prediction_experiments/exp_III_list_no_balance.RDS")
exp_III_both_list <- readRDS("prediction_experiments/exp_III_both_list_no_balance.RDS")
exp_III_add_list <- readRDS("prediction_experiments/exp_III_add_list_no_balance.RDS")


varimp_expI_traj <-varimp_function(exp_I_list, ex = "I", 
                      type = "", 
                      va ="(Traj)" ,
                      mycol = "steelblue1")

varimp_expI_traj_sum  <- varimp_function(exp_I_both_list, ex = "I", 
                      type = "both", 
                      va ="(Traj+Sum.Stat)",
                      mycol = "steelblue1")

varimp_expI_traj_sum_bl <- varimp_function(exp_I_add_list, ex = "I", 
                     type = "add", 
                     va ="(Traj+Sum.Stat+BL)",
                     mycol = "steelblue1")


varimp_expII_traj <- varimp_function(exp_II_list, ex = "II", 
                      type = "",
                      va ="(Traj)", 
                      mycol = "firebrick1")

varimp_expII_traj_sum <- varimp_function(exp_II_both_list, ex = "II", 
                      type = "both", 
                      va ="(Traj+Sum.Stat)",
                      mycol = "firebrick1")

varimp_expII_traj_sum_bl <-varimp_function(exp_II_add_list, ex = "II", 
                     type = "add", 
                     va ="(Traj+Sum.Stat+BL)",
                     mycol = "firebrick1")

varimp_expIII_traj <- varimp_function(exp_III_list, ex = "III", 
                      type = "", 
                      va ="(Traj)", 
                      mycol = "chartreuse3")

varimp_expIII_traj_sum <- varimp_function(exp_III_both_list, ex = "III", 
                      type = "both" ,
                      va ="(Traj+Sum.Stat)", 
                      mycol = "chartreuse3")

varimp_expIII_traj_sum_bl = varimp_function(exp_III_add_list, ex = "III", 
                      type = "add",
                      va ="(Traj+Sum.Stat+BL)",
                      mycol = "chartreuse3")


p_trj <- ggarrange(varimp_expI_traj, varimp_expII_traj, varimp_expIII_traj,
                  nrow=1, ncol=3, align = "h",
                  common.legend = TRUE, legend="right")

p_trjsum <- ggarrange(varimp_expI_traj_sum, varimp_expII_traj_sum, varimp_expIII_traj_sum,
                     nrow=1, ncol=3, align = "h",
                     common.legend = TRUE, legend="right")

p_trjsumbl <- ggarrange(varimp_expI_traj_sum_bl, varimp_expII_traj_sum_bl, varimp_expIII_traj_sum_bl,
                       nrow=1, ncol=3, align = "h",
                       common.legend = TRUE, legend="right")
ggsave(plot = p_trj, 
       paste0("figures/Variable Importance plots/varimp_figure_tr.png"), 
       width = 23, height = 26)

ggsave(plot = p_trjsum, 
       paste0("figures/Variable Importance plots/varimp_figure_trsum.png"), 
       width = 23, height = 26)

ggsave(plot = p_trjsumbl, 
       paste0("figures/Variable Importance plots/varimp_figure_trsumb.png"), 
       width = 23, height = 26)

p_trj

p_trjsum

p_trjsumbl

Code for modeling blood trajectories in SCI

Marzieh Mussavi Rizi, Abel Torres Espin

2025-05-04

Introduction

Setup

Libraries

Dependencies

Load libraries

The dataset build

Datasets

MIMIC

TRACK-SCI

MIMIC Patient selection

Creating ICD9/10 filtering

MIMIC III

MIMIC IV

Join both MIMIC

MIMIC laboratory marker data

MIMIC-III

MIMIC-IV

Join both MIMIC lab data

MIMIC patient characteristics

Filter of patients from Emergency only

Cohort definition

MIMIC Marker Exploratory Data Analysis (EDA)

Lab Marker summaries

Marker data cleaning

Selecting time window for analysis

Final MIMIC cohort

MIMIC Demographics table

MIMIC Stay characteristics

MIMIC-III vs. MIMIC-IV demographics

MIMIC-III vs. MIMIC-IV stay

TRACK-SCI data

Process data

TRACK table

TRACK-SCI spaghetti plots

Blood Trajectory Modeling in MIMIC

LCGA: Initial exploratory modeling

LCGA summaries

GMM: model search

GMM summaries

GMM: final models

Plotting trajectories

Trajectory description

Trajectory figure

ML Experiments

Prediction of trajectories

Exp I and II

Exp III

Experiment I

Blood trajectory feature set

Blood trajectory + summary stats feature set

Blood trajectory + summary stats + BL feature set

Summary performance

Hypothesis testing

Figures Exp I

Experiment II

Blood trajectory feature set

Blood trajectory + summary stats feature set

Blood trajectory + summary stats + BL feature set

Summary performance

Hypothesis testing

Figure Exp II

Experiment III

Blood trajectory feature set

Blood trajectory + summary stats feature set

Blood trajectory + summary stats + BL feature set

Summary performance

Hypothesis testing

Figure Exp III

Tables and Figures

Tables

Figures

Experiments with SAPSII

SAPSII Experiment I

SAPSII alone

Blood trajectory feature set

Blood trajectory + summary stats feature set

Blood trajectory + summary stats + BL feature set